Overview

Dataset statistics

Number of variables75
Number of observations1451
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory861.5 KiB
Average record size in memory608.0 B

Variable types

Numeric25
Categorical49
Boolean1

Alerts

1stFlrSF is highly overall correlated with SalePriceHigh correlation
2ndFlrSF is highly overall correlated with BedroomAbvGr and 1 other fieldsHigh correlation
BedroomAbvGr is highly overall correlated with 2ndFlrSF and 1 other fieldsHigh correlation
BsmtCond is highly overall correlated with BsmtFinType1 and 1 other fieldsHigh correlation
BsmtExposure is highly overall correlated with BsmtFinType1 and 1 other fieldsHigh correlation
BsmtFinSF1 is highly overall correlated with BsmtUnfSFHigh correlation
BsmtFinType1 is highly overall correlated with BsmtCond and 4 other fieldsHigh correlation
BsmtQual is highly overall correlated with BsmtCond and 6 other fieldsHigh correlation
BsmtUnfSF is highly overall correlated with BsmtFinSF1 and 1 other fieldsHigh correlation
ExterQual is highly overall correlated with KitchenQual and 3 other fieldsHigh correlation
Exterior1st is highly overall correlated with Exterior2ndHigh correlation
Exterior2nd is highly overall correlated with Exterior1stHigh correlation
FireplaceQu is highly overall correlated with FireplacesHigh correlation
Fireplaces is highly overall correlated with FireplaceQu and 1 other fieldsHigh correlation
Foundation is highly overall correlated with BsmtFinType1 and 4 other fieldsHigh correlation
FullBath is highly overall correlated with GrLivArea and 3 other fieldsHigh correlation
GarageArea is highly overall correlated with GarageCond and 7 other fieldsHigh correlation
GarageCond is highly overall correlated with GarageArea and 4 other fieldsHigh correlation
GarageFinish is highly overall correlated with GarageArea and 4 other fieldsHigh correlation
GarageQual is highly overall correlated with GarageArea and 4 other fieldsHigh correlation
GarageType is highly overall correlated with GarageArea and 5 other fieldsHigh correlation
GrLivArea is highly overall correlated with 2ndFlrSF and 4 other fieldsHigh correlation
HasGarage is highly overall correlated with GarageArea and 4 other fieldsHigh correlation
House Category is highly overall correlated with KitchenAbvGrHigh correlation
KitchenAbvGr is highly overall correlated with House CategoryHigh correlation
KitchenQual is highly overall correlated with ExterQual and 2 other fieldsHigh correlation
LotArea is highly overall correlated with LotFrontageHigh correlation
LotFrontage is highly overall correlated with LotAreaHigh correlation
MSZoning is highly overall correlated with NeighborhoodHigh correlation
MiscFeature is highly overall correlated with MiscValHigh correlation
MiscVal is highly overall correlated with MiscFeatureHigh correlation
Neighborhood is highly overall correlated with MSZoningHigh correlation
OverallQual is highly overall correlated with BsmtQual and 8 other fieldsHigh correlation
PoolArea is highly overall correlated with PoolQCHigh correlation
PoolQC is highly overall correlated with PoolAreaHigh correlation
RoofMatl is highly overall correlated with RoofStyleHigh correlation
RoofStyle is highly overall correlated with RoofMatlHigh correlation
SaleCondition is highly overall correlated with SaleTypeHigh correlation
SalePrice is highly overall correlated with 1stFlrSF and 11 other fieldsHigh correlation
SaleType is highly overall correlated with SaleConditionHigh correlation
YearsSinceGarage is highly overall correlated with BsmtQual and 6 other fieldsHigh correlation
Street is highly imbalanced (96.1%)Imbalance
Alley is highly imbalanced (66.5%)Imbalance
LandContour is highly imbalanced (68.2%)Imbalance
Utilities is highly imbalanced (99.2%)Imbalance
LandSlope is highly imbalanced (78.7%)Imbalance
Condition1 is highly imbalanced (55.0%)Imbalance
Condition2 is highly imbalanced (91.7%)Imbalance
RoofMatl is highly imbalanced (87.0%)Imbalance
ExterCond is highly imbalanced (72.7%)Imbalance
BsmtCond is highly imbalanced (72.4%)Imbalance
BsmtFinType2 is highly imbalanced (66.8%)Imbalance
Heating is highly imbalanced (84.7%)Imbalance
CentralAir is highly imbalanced (65.1%)Imbalance
Electrical is highly imbalanced (68.9%)Imbalance
BsmtHalfBath is highly imbalanced (79.6%)Imbalance
KitchenAbvGr is highly imbalanced (85.8%)Imbalance
Functional is highly imbalanced (64.1%)Imbalance
GarageQual is highly imbalanced (63.4%)Imbalance
GarageCond is highly imbalanced (66.3%)Imbalance
PavedDrive is highly imbalanced (69.8%)Imbalance
PoolQC is highly imbalanced (95.6%)Imbalance
MiscFeature is highly imbalanced (77.1%)Imbalance
SaleType is highly imbalanced (56.8%)Imbalance
SaleCondition is highly imbalanced (53.5%)Imbalance
HasGarage is highly imbalanced (68.9%)Imbalance
MiscVal is highly skewed (γ1 = 24.40151268)Skewed
MasVnrArea has 860 (59.3%) zerosZeros
BsmtFinSF1 has 464 (32.0%) zerosZeros
BsmtFinSF2 has 1284 (88.5%) zerosZeros
BsmtUnfSF has 118 (8.1%) zerosZeros
2ndFlrSF has 824 (56.8%) zerosZeros
LowQualFinSF has 1425 (98.2%) zerosZeros
GarageArea has 81 (5.6%) zerosZeros
WoodDeckSF has 755 (52.0%) zerosZeros
OpenPorchSF has 653 (45.0%) zerosZeros
EnclosedPorch has 1244 (85.7%) zerosZeros
3SsnPorch has 1427 (98.3%) zerosZeros
ScreenPorch has 1335 (92.0%) zerosZeros
PoolArea has 1444 (99.5%) zerosZeros
MiscVal has 1399 (96.4%) zerosZeros
YearsSinceGarage has 82 (5.7%) zerosZeros

Reproduction

Analysis started2024-05-03 04:22:26.459513
Analysis finished2024-05-03 04:25:35.782043
Duration3 minutes and 9.32 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

MSSubClass
Real number (ℝ)

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.93315
Minimum20
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:25:35.937853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median50
Q370
95-th percentile160
Maximum190
Range170
Interquartile range (IQR)50

Descriptive statistics

Standard deviation42.350366
Coefficient of variation (CV)0.74386128
Kurtosis1.577206
Mean56.93315
Median Absolute Deviation (MAD)30
Skewness1.4082993
Sum82610
Variance1793.5535
MonotonicityNot monotonic
2024-05-03T04:25:36.329629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20 532
36.7%
60 296
20.4%
50 144
 
9.9%
120 86
 
5.9%
30 69
 
4.8%
160 63
 
4.3%
70 60
 
4.1%
80 57
 
3.9%
90 52
 
3.6%
190 30
 
2.1%
Other values (5) 62
 
4.3%
ValueCountFrequency (%)
20 532
36.7%
30 69
 
4.8%
40 4
 
0.3%
45 12
 
0.8%
50 144
 
9.9%
60 296
20.4%
70 60
 
4.1%
75 16
 
1.1%
80 57
 
3.9%
85 20
 
1.4%
ValueCountFrequency (%)
190 30
 
2.1%
180 10
 
0.7%
160 63
 
4.3%
120 86
 
5.9%
90 52
 
3.6%
85 20
 
1.4%
80 57
 
3.9%
75 16
 
1.1%
70 60
 
4.1%
60 296
20.4%

MSZoning
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
RL
1145 
RM
218 
Other
 
88

Length

Max length5
Median length2
Mean length2.1819435
Min length2

Characters and Unicode

Total characters3166
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRL
2nd rowRL
3rd rowRL
4th rowRL
5th rowRL

Common Values

ValueCountFrequency (%)
RL 1145
78.9%
RM 218
 
15.0%
Other 88
 
6.1%

Length

2024-05-03T04:25:36.786263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:37.261951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rl 1145
78.9%
rm 218
 
15.0%
other 88
 
6.1%

Most occurring characters

ValueCountFrequency (%)
R 1363
43.1%
L 1145
36.2%
M 218
 
6.9%
O 88
 
2.8%
t 88
 
2.8%
h 88
 
2.8%
e 88
 
2.8%
r 88
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3166
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 1363
43.1%
L 1145
36.2%
M 218
 
6.9%
O 88
 
2.8%
t 88
 
2.8%
h 88
 
2.8%
e 88
 
2.8%
r 88
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3166
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 1363
43.1%
L 1145
36.2%
M 218
 
6.9%
O 88
 
2.8%
t 88
 
2.8%
h 88
 
2.8%
e 88
 
2.8%
r 88
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3166
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 1363
43.1%
L 1145
36.2%
M 218
 
6.9%
O 88
 
2.8%
t 88
 
2.8%
h 88
 
2.8%
e 88
 
2.8%
r 88
 
2.8%

LotFrontage
Real number (ℝ)

HIGH CORRELATION 

Distinct110
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.845624
Minimum21
Maximum313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:25:37.565593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile36
Q160
median69
Q379
95-th percentile104
Maximum313
Range292
Interquartile range (IQR)19

Descriptive statistics

Standard deviation22.044429
Coefficient of variation (CV)0.31561646
Kurtosis21.988965
Mean69.845624
Median Absolute Deviation (MAD)9
Skewness2.4194663
Sum101346
Variance485.95684
MonotonicityNot monotonic
2024-05-03T04:25:38.010084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69 268
18.5%
60 143
 
9.9%
70 70
 
4.8%
80 69
 
4.8%
50 57
 
3.9%
75 52
 
3.6%
65 43
 
3.0%
85 40
 
2.8%
78 25
 
1.7%
90 23
 
1.6%
Other values (100) 661
45.6%
ValueCountFrequency (%)
21 23
1.6%
24 19
1.3%
30 6
 
0.4%
32 5
 
0.3%
33 1
 
0.1%
34 10
0.7%
35 8
 
0.6%
36 6
 
0.4%
37 5
 
0.3%
38 1
 
0.1%
ValueCountFrequency (%)
313 2
0.1%
182 1
0.1%
174 2
0.1%
168 1
0.1%
160 1
0.1%
153 1
0.1%
152 1
0.1%
150 1
0.1%
149 1
0.1%
144 1
0.1%

LotArea
Real number (ℝ)

HIGH CORRELATION 

Distinct1066
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10507.808
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:25:38.402947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3273
Q17537.5
median9477
Q311600
95-th percentile17308.5
Maximum215245
Range213945
Interquartile range (IQR)4062.5

Descriptive statistics

Standard deviation9992.9871
Coefficient of variation (CV)0.95100583
Kurtosis203.58408
Mean10507.808
Median Absolute Deviation (MAD)2001
Skewness12.235742
Sum15246830
Variance99859791
MonotonicityNot monotonic
2024-05-03T04:25:38.687703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7200 25
 
1.7%
9600 24
 
1.7%
6000 17
 
1.2%
8400 14
 
1.0%
9000 14
 
1.0%
10800 14
 
1.0%
1680 10
 
0.7%
7500 9
 
0.6%
6120 8
 
0.6%
9100 8
 
0.6%
Other values (1056) 1308
90.1%
ValueCountFrequency (%)
1300 1
 
0.1%
1477 1
 
0.1%
1491 1
 
0.1%
1526 1
 
0.1%
1533 2
 
0.1%
1596 1
 
0.1%
1680 10
0.7%
1869 1
 
0.1%
1890 2
 
0.1%
1920 1
 
0.1%
ValueCountFrequency (%)
215245 1
0.1%
164660 1
0.1%
159000 1
0.1%
115149 1
0.1%
70761 1
0.1%
63887 1
0.1%
57200 1
0.1%
53504 1
0.1%
53227 1
0.1%
53107 1
0.1%

Street
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Pave
1445 
Other
 
6

Length

Max length5
Median length4
Mean length4.0041351
Min length4

Characters and Unicode

Total characters5810
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPave
2nd rowPave
3rd rowPave
4th rowPave
5th rowPave

Common Values

ValueCountFrequency (%)
Pave 1445
99.6%
Other 6
 
0.4%

Length

2024-05-03T04:25:38.942286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:39.170133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
pave 1445
99.6%
other 6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e 1451
25.0%
P 1445
24.9%
a 1445
24.9%
v 1445
24.9%
O 6
 
0.1%
t 6
 
0.1%
h 6
 
0.1%
r 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5810
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1451
25.0%
P 1445
24.9%
a 1445
24.9%
v 1445
24.9%
O 6
 
0.1%
t 6
 
0.1%
h 6
 
0.1%
r 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5810
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1451
25.0%
P 1445
24.9%
a 1445
24.9%
v 1445
24.9%
O 6
 
0.1%
t 6
 
0.1%
h 6
 
0.1%
r 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5810
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1451
25.0%
P 1445
24.9%
a 1445
24.9%
v 1445
24.9%
O 6
 
0.1%
t 6
 
0.1%
h 6
 
0.1%
r 6
 
0.1%

Alley
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
NoAlley
1361 
Other
 
90

Length

Max length7
Median length7
Mean length6.8759476
Min length5

Characters and Unicode

Total characters9977
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNoAlley
2nd rowNoAlley
3rd rowNoAlley
4th rowNoAlley
5th rowNoAlley

Common Values

ValueCountFrequency (%)
NoAlley 1361
93.8%
Other 90
 
6.2%

Length

2024-05-03T04:25:39.371436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:39.625960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
noalley 1361
93.8%
other 90
 
6.2%

Most occurring characters

ValueCountFrequency (%)
l 2722
27.3%
e 1451
14.5%
o 1361
13.6%
N 1361
13.6%
A 1361
13.6%
y 1361
13.6%
O 90
 
0.9%
t 90
 
0.9%
h 90
 
0.9%
r 90
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9977
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 2722
27.3%
e 1451
14.5%
o 1361
13.6%
N 1361
13.6%
A 1361
13.6%
y 1361
13.6%
O 90
 
0.9%
t 90
 
0.9%
h 90
 
0.9%
r 90
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9977
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 2722
27.3%
e 1451
14.5%
o 1361
13.6%
N 1361
13.6%
A 1361
13.6%
y 1361
13.6%
O 90
 
0.9%
t 90
 
0.9%
h 90
 
0.9%
r 90
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9977
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 2722
27.3%
e 1451
14.5%
o 1361
13.6%
N 1361
13.6%
A 1361
13.6%
y 1361
13.6%
O 90
 
0.9%
t 90
 
0.9%
h 90
 
0.9%
r 90
 
0.9%

LotShape
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Reg
918 
IR1
482 
IR2
 
41
IR3
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReg
2nd rowReg
3rd rowIR1
4th rowIR1
5th rowIR1

Common Values

ValueCountFrequency (%)
Reg 918
63.3%
IR1 482
33.2%
IR2 41
 
2.8%
IR3 10
 
0.7%

Length

2024-05-03T04:25:39.841830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:40.079973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
reg 918
63.3%
ir1 482
33.2%
ir2 41
 
2.8%
ir3 10
 
0.7%

Most occurring characters

ValueCountFrequency (%)
R 1451
33.3%
e 918
21.1%
g 918
21.1%
I 533
 
12.2%
1 482
 
11.1%
2 41
 
0.9%
3 10
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4353
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 1451
33.3%
e 918
21.1%
g 918
21.1%
I 533
 
12.2%
1 482
 
11.1%
2 41
 
0.9%
3 10
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4353
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 1451
33.3%
e 918
21.1%
g 918
21.1%
I 533
 
12.2%
1 482
 
11.1%
2 41
 
0.9%
3 10
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4353
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 1451
33.3%
e 918
21.1%
g 918
21.1%
I 533
 
12.2%
1 482
 
11.1%
2 41
 
0.9%
3 10
 
0.2%

LandContour
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Lvl
1302 
Bnk
 
63
HLS
 
50
Low
 
36

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLvl
2nd rowLvl
3rd rowLvl
4th rowLvl
5th rowLvl

Common Values

ValueCountFrequency (%)
Lvl 1302
89.7%
Bnk 63
 
4.3%
HLS 50
 
3.4%
Low 36
 
2.5%

Length

2024-05-03T04:25:40.296992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:40.530643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
lvl 1302
89.7%
bnk 63
 
4.3%
hls 50
 
3.4%
low 36
 
2.5%

Most occurring characters

ValueCountFrequency (%)
L 1388
31.9%
v 1302
29.9%
l 1302
29.9%
B 63
 
1.4%
n 63
 
1.4%
k 63
 
1.4%
H 50
 
1.1%
S 50
 
1.1%
o 36
 
0.8%
w 36
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4353
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 1388
31.9%
v 1302
29.9%
l 1302
29.9%
B 63
 
1.4%
n 63
 
1.4%
k 63
 
1.4%
H 50
 
1.1%
S 50
 
1.1%
o 36
 
0.8%
w 36
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4353
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 1388
31.9%
v 1302
29.9%
l 1302
29.9%
B 63
 
1.4%
n 63
 
1.4%
k 63
 
1.4%
H 50
 
1.1%
S 50
 
1.1%
o 36
 
0.8%
w 36
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4353
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 1388
31.9%
v 1302
29.9%
l 1302
29.9%
B 63
 
1.4%
n 63
 
1.4%
k 63
 
1.4%
H 50
 
1.1%
S 50
 
1.1%
o 36
 
0.8%
w 36
 
0.8%

Utilities
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
AllPub
1450 
NoSeWa
 
1

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters8706
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAllPub
2nd rowAllPub
3rd rowAllPub
4th rowAllPub
5th rowAllPub

Common Values

ValueCountFrequency (%)
AllPub 1450
99.9%
NoSeWa 1
 
0.1%

Length

2024-05-03T04:25:40.744273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:40.971658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
allpub 1450
99.9%
nosewa 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
l 2900
33.3%
A 1450
16.7%
P 1450
16.7%
u 1450
16.7%
b 1450
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8706
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 2900
33.3%
A 1450
16.7%
P 1450
16.7%
u 1450
16.7%
b 1450
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8706
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 2900
33.3%
A 1450
16.7%
P 1450
16.7%
u 1450
16.7%
b 1450
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8706
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 2900
33.3%
A 1450
16.7%
P 1450
16.7%
u 1450
16.7%
b 1450
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

LotConfig
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Inside
1045 
Corner
262 
CulDSac
 
93
Other
 
51

Length

Max length7
Median length6
Mean length6.0289456
Min length5

Characters and Unicode

Total characters8748
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInside
2nd rowOther
3rd rowInside
4th rowCorner
5th rowOther

Common Values

ValueCountFrequency (%)
Inside 1045
72.0%
Corner 262
 
18.1%
CulDSac 93
 
6.4%
Other 51
 
3.5%

Length

2024-05-03T04:25:41.181067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:41.446982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
inside 1045
72.0%
corner 262
 
18.1%
culdsac 93
 
6.4%
other 51
 
3.5%

Most occurring characters

ValueCountFrequency (%)
e 1358
15.5%
n 1307
14.9%
I 1045
11.9%
s 1045
11.9%
i 1045
11.9%
d 1045
11.9%
r 575
6.6%
C 355
 
4.1%
o 262
 
3.0%
u 93
 
1.1%
Other values (8) 618
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8748
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1358
15.5%
n 1307
14.9%
I 1045
11.9%
s 1045
11.9%
i 1045
11.9%
d 1045
11.9%
r 575
6.6%
C 355
 
4.1%
o 262
 
3.0%
u 93
 
1.1%
Other values (8) 618
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8748
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1358
15.5%
n 1307
14.9%
I 1045
11.9%
s 1045
11.9%
i 1045
11.9%
d 1045
11.9%
r 575
6.6%
C 355
 
4.1%
o 262
 
3.0%
u 93
 
1.1%
Other values (8) 618
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8748
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1358
15.5%
n 1307
14.9%
I 1045
11.9%
s 1045
11.9%
i 1045
11.9%
d 1045
11.9%
r 575
6.6%
C 355
 
4.1%
o 262
 
3.0%
u 93
 
1.1%
Other values (8) 618
7.1%

LandSlope
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Gtl
1373 
Mod
 
65
Sev
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4353
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGtl
2nd rowGtl
3rd rowGtl
4th rowGtl
5th rowGtl

Common Values

ValueCountFrequency (%)
Gtl 1373
94.6%
Mod 65
 
4.5%
Sev 13
 
0.9%

Length

2024-05-03T04:25:41.649523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:41.889907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
gtl 1373
94.6%
mod 65
 
4.5%
sev 13
 
0.9%

Most occurring characters

ValueCountFrequency (%)
G 1373
31.5%
t 1373
31.5%
l 1373
31.5%
M 65
 
1.5%
o 65
 
1.5%
d 65
 
1.5%
S 13
 
0.3%
e 13
 
0.3%
v 13
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4353
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 1373
31.5%
t 1373
31.5%
l 1373
31.5%
M 65
 
1.5%
o 65
 
1.5%
d 65
 
1.5%
S 13
 
0.3%
e 13
 
0.3%
v 13
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4353
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 1373
31.5%
t 1373
31.5%
l 1373
31.5%
M 65
 
1.5%
o 65
 
1.5%
d 65
 
1.5%
S 13
 
0.3%
e 13
 
0.3%
v 13
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4353
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 1373
31.5%
t 1373
31.5%
l 1373
31.5%
M 65
 
1.5%
o 65
 
1.5%
d 65
 
1.5%
S 13
 
0.3%
e 13
 
0.3%
v 13
 
0.3%

Neighborhood
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Other
480 
NAmes
225 
CollgCr
149 
OldTown
113 
Edwards
100 
Other values (5)
384 

Length

Max length7
Median length6
Mean length5.9269469
Min length5

Characters and Unicode

Total characters8600
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCollgCr
2nd rowOther
3rd rowCollgCr
4th rowOther
5th rowOther

Common Values

ValueCountFrequency (%)
Other 480
33.1%
NAmes 225
15.5%
CollgCr 149
 
10.3%
OldTown 113
 
7.8%
Edwards 100
 
6.9%
Somerst 83
 
5.7%
Gilbert 78
 
5.4%
NridgHt 76
 
5.2%
Sawyer 74
 
5.1%
NWAmes 73
 
5.0%

Length

2024-05-03T04:25:42.102960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:42.408010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
other 480
33.1%
names 225
15.5%
collgcr 149
 
10.3%
oldtown 113
 
7.8%
edwards 100
 
6.9%
somerst 83
 
5.7%
gilbert 78
 
5.4%
nridght 76
 
5.2%
sawyer 74
 
5.1%
nwames 73
 
5.0%

Most occurring characters

ValueCountFrequency (%)
r 1040
 
12.1%
e 1013
 
11.8%
t 717
 
8.3%
O 593
 
6.9%
l 489
 
5.7%
s 481
 
5.6%
h 480
 
5.6%
d 389
 
4.5%
m 381
 
4.4%
N 374
 
4.3%
Other values (16) 2643
30.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8600
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1040
 
12.1%
e 1013
 
11.8%
t 717
 
8.3%
O 593
 
6.9%
l 489
 
5.7%
s 481
 
5.6%
h 480
 
5.6%
d 389
 
4.5%
m 381
 
4.4%
N 374
 
4.3%
Other values (16) 2643
30.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8600
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1040
 
12.1%
e 1013
 
11.8%
t 717
 
8.3%
O 593
 
6.9%
l 489
 
5.7%
s 481
 
5.6%
h 480
 
5.6%
d 389
 
4.5%
m 381
 
4.4%
N 374
 
4.3%
Other values (16) 2643
30.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8600
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1040
 
12.1%
e 1013
 
11.8%
t 717
 
8.3%
O 593
 
6.9%
l 489
 
5.7%
s 481
 
5.6%
h 480
 
5.6%
d 389
 
4.5%
m 381
 
4.4%
N 374
 
4.3%
Other values (16) 2643
30.7%

Condition1
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Norm
1251 
Other
 
119
Feedr
 
81

Length

Max length5
Median length4
Mean length4.137836
Min length4

Characters and Unicode

Total characters6004
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorm
2nd rowFeedr
3rd rowNorm
4th rowNorm
5th rowNorm

Common Values

ValueCountFrequency (%)
Norm 1251
86.2%
Other 119
 
8.2%
Feedr 81
 
5.6%

Length

2024-05-03T04:25:42.683118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:42.932715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
norm 1251
86.2%
other 119
 
8.2%
feedr 81
 
5.6%

Most occurring characters

ValueCountFrequency (%)
r 1451
24.2%
N 1251
20.8%
o 1251
20.8%
m 1251
20.8%
e 281
 
4.7%
O 119
 
2.0%
t 119
 
2.0%
h 119
 
2.0%
F 81
 
1.3%
d 81
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6004
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1451
24.2%
N 1251
20.8%
o 1251
20.8%
m 1251
20.8%
e 281
 
4.7%
O 119
 
2.0%
t 119
 
2.0%
h 119
 
2.0%
F 81
 
1.3%
d 81
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6004
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1451
24.2%
N 1251
20.8%
o 1251
20.8%
m 1251
20.8%
e 281
 
4.7%
O 119
 
2.0%
t 119
 
2.0%
h 119
 
2.0%
F 81
 
1.3%
d 81
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6004
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1451
24.2%
N 1251
20.8%
o 1251
20.8%
m 1251
20.8%
e 281
 
4.7%
O 119
 
2.0%
t 119
 
2.0%
h 119
 
2.0%
F 81
 
1.3%
d 81
 
1.3%

Condition2
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Norm
1436 
Other
 
15

Length

Max length5
Median length4
Mean length4.0103377
Min length4

Characters and Unicode

Total characters5819
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorm
2nd rowNorm
3rd rowNorm
4th rowNorm
5th rowNorm

Common Values

ValueCountFrequency (%)
Norm 1436
99.0%
Other 15
 
1.0%

Length

2024-05-03T04:25:43.144192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:43.373979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
norm 1436
99.0%
other 15
 
1.0%

Most occurring characters

ValueCountFrequency (%)
r 1451
24.9%
N 1436
24.7%
o 1436
24.7%
m 1436
24.7%
O 15
 
0.3%
t 15
 
0.3%
h 15
 
0.3%
e 15
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5819
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1451
24.9%
N 1436
24.7%
o 1436
24.7%
m 1436
24.7%
O 15
 
0.3%
t 15
 
0.3%
h 15
 
0.3%
e 15
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5819
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1451
24.9%
N 1436
24.7%
o 1436
24.7%
m 1436
24.7%
O 15
 
0.3%
t 15
 
0.3%
h 15
 
0.3%
e 15
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5819
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1451
24.9%
N 1436
24.7%
o 1436
24.7%
m 1436
24.7%
O 15
 
0.3%
t 15
 
0.3%
h 15
 
0.3%
e 15
 
0.3%

OverallQual
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0937285
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:25:43.548595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3814667
Coefficient of variation (CV)0.22670303
Kurtosis0.085527293
Mean6.0937285
Median Absolute Deviation (MAD)1
Skewness0.21340488
Sum8842
Variance1.9084503
MonotonicityNot monotonic
2024-05-03T04:25:43.734315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 396
27.3%
6 372
25.6%
7 315
21.7%
8 167
11.5%
4 116
 
8.0%
9 43
 
3.0%
3 20
 
1.4%
10 17
 
1.2%
2 3
 
0.2%
1 2
 
0.1%
ValueCountFrequency (%)
1 2
 
0.1%
2 3
 
0.2%
3 20
 
1.4%
4 116
 
8.0%
5 396
27.3%
6 372
25.6%
7 315
21.7%
8 167
11.5%
9 43
 
3.0%
10 17
 
1.2%
ValueCountFrequency (%)
10 17
 
1.2%
9 43
 
3.0%
8 167
11.5%
7 315
21.7%
6 372
25.6%
5 396
27.3%
4 116
 
8.0%
3 20
 
1.4%
2 3
 
0.2%
1 2
 
0.1%

OverallCond
Real number (ℝ)

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5796003
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:25:43.941168image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1134154
Coefficient of variation (CV)0.19955111
Kurtosis1.0919419
Mean5.5796003
Median Absolute Deviation (MAD)0
Skewness0.69390475
Sum8096
Variance1.2396939
MonotonicityNot monotonic
2024-05-03T04:25:44.132286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 814
56.1%
6 251
 
17.3%
7 205
 
14.1%
8 72
 
5.0%
4 57
 
3.9%
3 24
 
1.7%
9 22
 
1.5%
2 5
 
0.3%
1 1
 
0.1%
ValueCountFrequency (%)
1 1
 
0.1%
2 5
 
0.3%
3 24
 
1.7%
4 57
 
3.9%
5 814
56.1%
6 251
 
17.3%
7 205
 
14.1%
8 72
 
5.0%
9 22
 
1.5%
ValueCountFrequency (%)
9 22
 
1.5%
8 72
 
5.0%
7 205
 
14.1%
6 251
 
17.3%
5 814
56.1%
4 57
 
3.9%
3 24
 
1.7%
2 5
 
0.3%
1 1
 
0.1%

RoofStyle
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Gable
1133 
Hip
285 
Other
 
33

Length

Max length5
Median length5
Mean length4.6071675
Min length3

Characters and Unicode

Total characters6685
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGable
2nd rowGable
3rd rowGable
4th rowGable
5th rowGable

Common Values

ValueCountFrequency (%)
Gable 1133
78.1%
Hip 285
 
19.6%
Other 33
 
2.3%

Length

2024-05-03T04:25:44.384811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:44.637065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
gable 1133
78.1%
hip 285
 
19.6%
other 33
 
2.3%

Most occurring characters

ValueCountFrequency (%)
e 1166
17.4%
G 1133
16.9%
b 1133
16.9%
a 1133
16.9%
l 1133
16.9%
H 285
 
4.3%
i 285
 
4.3%
p 285
 
4.3%
O 33
 
0.5%
t 33
 
0.5%
Other values (2) 66
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6685
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1166
17.4%
G 1133
16.9%
b 1133
16.9%
a 1133
16.9%
l 1133
16.9%
H 285
 
4.3%
i 285
 
4.3%
p 285
 
4.3%
O 33
 
0.5%
t 33
 
0.5%
Other values (2) 66
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6685
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1166
17.4%
G 1133
16.9%
b 1133
16.9%
a 1133
16.9%
l 1133
16.9%
H 285
 
4.3%
i 285
 
4.3%
p 285
 
4.3%
O 33
 
0.5%
t 33
 
0.5%
Other values (2) 66
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6685
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1166
17.4%
G 1133
16.9%
b 1133
16.9%
a 1133
16.9%
l 1133
16.9%
H 285
 
4.3%
i 285
 
4.3%
p 285
 
4.3%
O 33
 
0.5%
t 33
 
0.5%
Other values (2) 66
 
1.0%

RoofMatl
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
CompShg
1425 
Other
 
26

Length

Max length7
Median length7
Mean length6.9641626
Min length5

Characters and Unicode

Total characters10105
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompShg
2nd rowCompShg
3rd rowCompShg
4th rowCompShg
5th rowCompShg

Common Values

ValueCountFrequency (%)
CompShg 1425
98.2%
Other 26
 
1.8%

Length

2024-05-03T04:25:44.880882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:45.130946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
compshg 1425
98.2%
other 26
 
1.8%

Most occurring characters

ValueCountFrequency (%)
h 1451
14.4%
o 1425
14.1%
C 1425
14.1%
m 1425
14.1%
p 1425
14.1%
S 1425
14.1%
g 1425
14.1%
O 26
 
0.3%
t 26
 
0.3%
e 26
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10105
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
h 1451
14.4%
o 1425
14.1%
C 1425
14.1%
m 1425
14.1%
p 1425
14.1%
S 1425
14.1%
g 1425
14.1%
O 26
 
0.3%
t 26
 
0.3%
e 26
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10105
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
h 1451
14.4%
o 1425
14.1%
C 1425
14.1%
m 1425
14.1%
p 1425
14.1%
S 1425
14.1%
g 1425
14.1%
O 26
 
0.3%
t 26
 
0.3%
e 26
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10105
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
h 1451
14.4%
o 1425
14.1%
C 1425
14.1%
m 1425
14.1%
p 1425
14.1%
S 1425
14.1%
g 1425
14.1%
O 26
 
0.3%
t 26
 
0.3%
e 26
 
0.3%

Exterior1st
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
VinylSd
509 
HdBoard
222 
MetalSd
220 
Wd Sdng
205 
Other
187 

Length

Max length7
Median length7
Mean length6.7422467
Min length5

Characters and Unicode

Total characters9783
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Sdng
5th rowVinylSd

Common Values

ValueCountFrequency (%)
VinylSd 509
35.1%
HdBoard 222
15.3%
MetalSd 220
15.2%
Wd Sdng 205
14.1%
Other 187
 
12.9%
Plywood 108
 
7.4%

Length

2024-05-03T04:25:45.336686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:45.628941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
vinylsd 509
30.7%
hdboard 222
13.4%
metalsd 220
13.3%
wd 205
12.4%
sdng 205
12.4%
other 187
 
11.3%
plywood 108
 
6.5%

Most occurring characters

ValueCountFrequency (%)
d 1691
17.3%
S 934
 
9.5%
l 837
 
8.6%
n 714
 
7.3%
y 617
 
6.3%
i 509
 
5.2%
V 509
 
5.2%
a 442
 
4.5%
o 438
 
4.5%
r 409
 
4.2%
Other values (12) 2683
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9783
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 1691
17.3%
S 934
 
9.5%
l 837
 
8.6%
n 714
 
7.3%
y 617
 
6.3%
i 509
 
5.2%
V 509
 
5.2%
a 442
 
4.5%
o 438
 
4.5%
r 409
 
4.2%
Other values (12) 2683
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9783
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 1691
17.3%
S 934
 
9.5%
l 837
 
8.6%
n 714
 
7.3%
y 617
 
6.3%
i 509
 
5.2%
V 509
 
5.2%
a 442
 
4.5%
o 438
 
4.5%
r 409
 
4.2%
Other values (12) 2683
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9783
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 1691
17.3%
S 934
 
9.5%
l 837
 
8.6%
n 714
 
7.3%
y 617
 
6.3%
i 509
 
5.2%
V 509
 
5.2%
a 442
 
4.5%
o 438
 
4.5%
r 409
 
4.2%
Other values (12) 2683
27.4%

Exterior2nd
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
VinylSd
498 
MetalSd
214 
HdBoard
207 
Wd Sdng
197 
Other
193 

Length

Max length7
Median length7
Mean length6.7339766
Min length5

Characters and Unicode

Total characters9771
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowOther
5th rowVinylSd

Common Values

ValueCountFrequency (%)
VinylSd 498
34.3%
MetalSd 214
14.7%
HdBoard 207
14.3%
Wd Sdng 197
 
13.6%
Other 193
 
13.3%
Plywood 142
 
9.8%

Length

2024-05-03T04:25:45.923503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:46.216024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
vinylsd 498
30.2%
metalsd 214
13.0%
hdboard 207
12.6%
wd 197
 
12.0%
sdng 197
 
12.0%
other 193
 
11.7%
plywood 142
 
8.6%

Most occurring characters

ValueCountFrequency (%)
d 1662
17.0%
S 909
 
9.3%
l 854
 
8.7%
n 695
 
7.1%
y 640
 
6.5%
i 498
 
5.1%
V 498
 
5.1%
o 491
 
5.0%
a 421
 
4.3%
e 407
 
4.2%
Other values (12) 2696
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9771
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 1662
17.0%
S 909
 
9.3%
l 854
 
8.7%
n 695
 
7.1%
y 640
 
6.5%
i 498
 
5.1%
V 498
 
5.1%
o 491
 
5.0%
a 421
 
4.3%
e 407
 
4.2%
Other values (12) 2696
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9771
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 1662
17.0%
S 909
 
9.3%
l 854
 
8.7%
n 695
 
7.1%
y 640
 
6.5%
i 498
 
5.1%
V 498
 
5.1%
o 491
 
5.0%
a 421
 
4.3%
e 407
 
4.2%
Other values (12) 2696
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9771
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 1662
17.0%
S 909
 
9.3%
l 854
 
8.7%
n 695
 
7.1%
y 640
 
6.5%
i 498
 
5.1%
V 498
 
5.1%
o 491
 
5.0%
a 421
 
4.3%
e 407
 
4.2%
Other values (12) 2696
27.6%

MasVnrType
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
NoMasVnr
863 
BrkFace
445 
Stone
128 
Other
 
15

Length

Max length8
Median length8
Mean length7.3976568
Min length5

Characters and Unicode

Total characters10734
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrkFace
2nd rowNoMasVnr
3rd rowBrkFace
4th rowNoMasVnr
5th rowBrkFace

Common Values

ValueCountFrequency (%)
NoMasVnr 863
59.5%
BrkFace 445
30.7%
Stone 128
 
8.8%
Other 15
 
1.0%

Length

2024-05-03T04:25:46.487352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:46.739414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nomasvnr 863
59.5%
brkface 445
30.7%
stone 128
 
8.8%
other 15
 
1.0%

Most occurring characters

ValueCountFrequency (%)
r 1323
12.3%
a 1308
12.2%
n 991
9.2%
o 991
9.2%
N 863
8.0%
s 863
8.0%
M 863
8.0%
V 863
8.0%
e 588
 
5.5%
B 445
 
4.1%
Other values (7) 1636
15.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10734
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1323
12.3%
a 1308
12.2%
n 991
9.2%
o 991
9.2%
N 863
8.0%
s 863
8.0%
M 863
8.0%
V 863
8.0%
e 588
 
5.5%
B 445
 
4.1%
Other values (7) 1636
15.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10734
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1323
12.3%
a 1308
12.2%
n 991
9.2%
o 991
9.2%
N 863
8.0%
s 863
8.0%
M 863
8.0%
V 863
8.0%
e 588
 
5.5%
B 445
 
4.1%
Other values (7) 1636
15.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10734
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1323
12.3%
a 1308
12.2%
n 991
9.2%
o 991
9.2%
N 863
8.0%
s 863
8.0%
M 863
8.0%
V 863
8.0%
e 588
 
5.5%
B 445
 
4.1%
Other values (7) 1636
15.2%

MasVnrArea
Real number (ℝ)

ZEROS 

Distinct327
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.75672
Minimum0
Maximum1600
Zeros860
Zeros (%)59.3%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:25:46.994708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3166
95-th percentile456
Maximum1600
Range1600
Interquartile range (IQR)166

Descriptive statistics

Standard deviation181.10815
Coefficient of variation (CV)1.7455077
Kurtosis10.07505
Mean103.75672
Median Absolute Deviation (MAD)0
Skewness2.6680165
Sum150551
Variance32800.162
MonotonicityNot monotonic
2024-05-03T04:25:47.265594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 860
59.3%
180 8
 
0.6%
108 8
 
0.6%
72 8
 
0.6%
16 7
 
0.5%
120 7
 
0.5%
200 6
 
0.4%
340 6
 
0.4%
106 6
 
0.4%
80 6
 
0.4%
Other values (317) 529
36.5%
ValueCountFrequency (%)
0 860
59.3%
1 2
 
0.1%
11 1
 
0.1%
14 1
 
0.1%
16 7
 
0.5%
18 2
 
0.1%
22 1
 
0.1%
24 1
 
0.1%
27 1
 
0.1%
28 1
 
0.1%
ValueCountFrequency (%)
1600 1
0.1%
1378 1
0.1%
1170 1
0.1%
1129 1
0.1%
1115 1
0.1%
1047 1
0.1%
1031 1
0.1%
975 1
0.1%
922 1
0.1%
921 1
0.1%

ExterQual
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
TA
905 
Gd
481 
Ex
 
51
Fa
 
14

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2902
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA 905
62.4%
Gd 481
33.1%
Ex 51
 
3.5%
Fa 14
 
1.0%

Length

2024-05-03T04:25:47.527521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:47.761480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ta 905
62.4%
gd 481
33.1%
ex 51
 
3.5%
fa 14
 
1.0%

Most occurring characters

ValueCountFrequency (%)
T 905
31.2%
A 905
31.2%
G 481
16.6%
d 481
16.6%
E 51
 
1.8%
x 51
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2902
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 905
31.2%
A 905
31.2%
G 481
16.6%
d 481
16.6%
E 51
 
1.8%
x 51
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2902
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 905
31.2%
A 905
31.2%
G 481
16.6%
d 481
16.6%
E 51
 
1.8%
x 51
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2902
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 905
31.2%
A 905
31.2%
G 481
16.6%
d 481
16.6%
E 51
 
1.8%
x 51
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

ExterCond
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
TA
1273 
Gd
146 
Fa
 
28
Ex
 
3
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2902
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1273
87.7%
Gd 146
 
10.1%
Fa 28
 
1.9%
Ex 3
 
0.2%
Po 1
 
0.1%

Length

2024-05-03T04:25:48.004436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:48.264092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ta 1273
87.7%
gd 146
 
10.1%
fa 28
 
1.9%
ex 3
 
0.2%
po 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1273
43.9%
A 1273
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2902
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1273
43.9%
A 1273
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2902
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1273
43.9%
A 1273
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2902
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1273
43.9%
A 1273
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Foundation
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
PConc
638 
CBlock
634 
BrkTil
146 
Other
 
33

Length

Max length6
Median length6
Mean length5.5375603
Min length5

Characters and Unicode

Total characters8035
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPConc
2nd rowCBlock
3rd rowPConc
4th rowBrkTil
5th rowPConc

Common Values

ValueCountFrequency (%)
PConc 638
44.0%
CBlock 634
43.7%
BrkTil 146
 
10.1%
Other 33
 
2.3%

Length

2024-05-03T04:25:48.575770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:48.962083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
pconc 638
44.0%
cblock 634
43.7%
brktil 146
 
10.1%
other 33
 
2.3%

Most occurring characters

ValueCountFrequency (%)
C 1272
15.8%
o 1272
15.8%
c 1272
15.8%
l 780
9.7%
B 780
9.7%
k 780
9.7%
P 638
7.9%
n 638
7.9%
r 179
 
2.2%
T 146
 
1.8%
Other values (5) 278
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8035
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 1272
15.8%
o 1272
15.8%
c 1272
15.8%
l 780
9.7%
B 780
9.7%
k 780
9.7%
P 638
7.9%
n 638
7.9%
r 179
 
2.2%
T 146
 
1.8%
Other values (5) 278
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8035
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 1272
15.8%
o 1272
15.8%
c 1272
15.8%
l 780
9.7%
B 780
9.7%
k 780
9.7%
P 638
7.9%
n 638
7.9%
r 179
 
2.2%
T 146
 
1.8%
Other values (5) 278
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8035
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 1272
15.8%
o 1272
15.8%
c 1272
15.8%
l 780
9.7%
B 780
9.7%
k 780
9.7%
P 638
7.9%
n 638
7.9%
r 179
 
2.2%
T 146
 
1.8%
Other values (5) 278
 
3.5%

BsmtQual
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
TA
648 
Gd
611 
Ex
120 
NoBsmt
 
37
Fa
 
35

Length

Max length6
Median length2
Mean length2.1019986
Min length2

Characters and Unicode

Total characters3050
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowGd
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA 648
44.7%
Gd 611
42.1%
Ex 120
 
8.3%
NoBsmt 37
 
2.5%
Fa 35
 
2.4%

Length

2024-05-03T04:25:49.415347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:49.833187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ta 648
44.7%
gd 611
42.1%
ex 120
 
8.3%
nobsmt 37
 
2.5%
fa 35
 
2.4%

Most occurring characters

ValueCountFrequency (%)
T 648
21.2%
A 648
21.2%
G 611
20.0%
d 611
20.0%
E 120
 
3.9%
x 120
 
3.9%
N 37
 
1.2%
o 37
 
1.2%
B 37
 
1.2%
s 37
 
1.2%
Other values (4) 144
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3050
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 648
21.2%
A 648
21.2%
G 611
20.0%
d 611
20.0%
E 120
 
3.9%
x 120
 
3.9%
N 37
 
1.2%
o 37
 
1.2%
B 37
 
1.2%
s 37
 
1.2%
Other values (4) 144
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3050
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 648
21.2%
A 648
21.2%
G 611
20.0%
d 611
20.0%
E 120
 
3.9%
x 120
 
3.9%
N 37
 
1.2%
o 37
 
1.2%
B 37
 
1.2%
s 37
 
1.2%
Other values (4) 144
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3050
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 648
21.2%
A 648
21.2%
G 611
20.0%
d 611
20.0%
E 120
 
3.9%
x 120
 
3.9%
N 37
 
1.2%
o 37
 
1.2%
B 37
 
1.2%
s 37
 
1.2%
Other values (4) 144
 
4.7%

BsmtCond
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
TA
1303 
Gd
 
64
Fa
 
45
NoBsmt
 
37
Po
 
2

Length

Max length6
Median length2
Mean length2.1019986
Min length2

Characters and Unicode

Total characters3050
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowGd
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1303
89.8%
Gd 64
 
4.4%
Fa 45
 
3.1%
NoBsmt 37
 
2.5%
Po 2
 
0.1%

Length

2024-05-03T04:25:50.299495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:50.758731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ta 1303
89.8%
gd 64
 
4.4%
fa 45
 
3.1%
nobsmt 37
 
2.5%
po 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1303
42.7%
A 1303
42.7%
G 64
 
2.1%
d 64
 
2.1%
F 45
 
1.5%
a 45
 
1.5%
o 39
 
1.3%
N 37
 
1.2%
B 37
 
1.2%
s 37
 
1.2%
Other values (3) 76
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3050
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1303
42.7%
A 1303
42.7%
G 64
 
2.1%
d 64
 
2.1%
F 45
 
1.5%
a 45
 
1.5%
o 39
 
1.3%
N 37
 
1.2%
B 37
 
1.2%
s 37
 
1.2%
Other values (3) 76
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3050
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1303
42.7%
A 1303
42.7%
G 64
 
2.1%
d 64
 
2.1%
F 45
 
1.5%
a 45
 
1.5%
o 39
 
1.3%
N 37
 
1.2%
B 37
 
1.2%
s 37
 
1.2%
Other values (3) 76
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3050
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1303
42.7%
A 1303
42.7%
G 64
 
2.1%
d 64
 
2.1%
F 45
 
1.5%
a 45
 
1.5%
o 39
 
1.3%
N 37
 
1.2%
B 37
 
1.2%
s 37
 
1.2%
Other values (3) 76
 
2.5%

BsmtExposure
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
No
945 
Av
221 
Gd
133 
Mn
114 
Other
 
38

Length

Max length5
Median length2
Mean length2.0785665
Min length2

Characters and Unicode

Total characters3016
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowGd
3rd rowMn
4th rowNo
5th rowAv

Common Values

ValueCountFrequency (%)
No 945
65.1%
Av 221
 
15.2%
Gd 133
 
9.2%
Mn 114
 
7.9%
Other 38
 
2.6%

Length

2024-05-03T04:25:51.192723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:51.646120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
no 945
65.1%
av 221
 
15.2%
gd 133
 
9.2%
mn 114
 
7.9%
other 38
 
2.6%

Most occurring characters

ValueCountFrequency (%)
N 945
31.3%
o 945
31.3%
A 221
 
7.3%
v 221
 
7.3%
G 133
 
4.4%
d 133
 
4.4%
M 114
 
3.8%
n 114
 
3.8%
O 38
 
1.3%
t 38
 
1.3%
Other values (3) 114
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3016
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 945
31.3%
o 945
31.3%
A 221
 
7.3%
v 221
 
7.3%
G 133
 
4.4%
d 133
 
4.4%
M 114
 
3.8%
n 114
 
3.8%
O 38
 
1.3%
t 38
 
1.3%
Other values (3) 114
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3016
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 945
31.3%
o 945
31.3%
A 221
 
7.3%
v 221
 
7.3%
G 133
 
4.4%
d 133
 
4.4%
M 114
 
3.8%
n 114
 
3.8%
O 38
 
1.3%
t 38
 
1.3%
Other values (3) 114
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3016
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 945
31.3%
o 945
31.3%
A 221
 
7.3%
v 221
 
7.3%
G 133
 
4.4%
d 133
 
4.4%
M 114
 
3.8%
n 114
 
3.8%
O 38
 
1.3%
t 38
 
1.3%
Other values (3) 114
 
3.8%

BsmtFinType1
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Unf
427 
GLQ
413 
ALQ
220 
BLQ
148 
Rec
132 
Other values (2)
111 

Length

Max length6
Median length3
Mean length3.076499
Min length3

Characters and Unicode

Total characters4464
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGLQ
2nd rowALQ
3rd rowGLQ
4th rowALQ
5th rowGLQ

Common Values

ValueCountFrequency (%)
Unf 427
29.4%
GLQ 413
28.5%
ALQ 220
15.2%
BLQ 148
 
10.2%
Rec 132
 
9.1%
LwQ 74
 
5.1%
NoBsmt 37
 
2.5%

Length

2024-05-03T04:25:51.899982image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:52.194043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
unf 427
29.4%
glq 413
28.5%
alq 220
15.2%
blq 148
 
10.2%
rec 132
 
9.1%
lwq 74
 
5.1%
nobsmt 37
 
2.5%

Most occurring characters

ValueCountFrequency (%)
L 855
19.2%
Q 855
19.2%
U 427
9.6%
f 427
9.6%
n 427
9.6%
G 413
9.3%
A 220
 
4.9%
B 185
 
4.1%
R 132
 
3.0%
e 132
 
3.0%
Other values (7) 391
8.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4464
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 855
19.2%
Q 855
19.2%
U 427
9.6%
f 427
9.6%
n 427
9.6%
G 413
9.3%
A 220
 
4.9%
B 185
 
4.1%
R 132
 
3.0%
e 132
 
3.0%
Other values (7) 391
8.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4464
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 855
19.2%
Q 855
19.2%
U 427
9.6%
f 427
9.6%
n 427
9.6%
G 413
9.3%
A 220
 
4.9%
B 185
 
4.1%
R 132
 
3.0%
e 132
 
3.0%
Other values (7) 391
8.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4464
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 855
19.2%
Q 855
19.2%
U 427
9.6%
f 427
9.6%
n 427
9.6%
G 413
9.3%
A 220
 
4.9%
B 185
 
4.1%
R 132
 
3.0%
e 132
 
3.0%
Other values (7) 391
8.8%

BsmtFinSF1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct633
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean442.27498
Minimum0
Maximum5644
Zeros464
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:25:52.464625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median381
Q3707
95-th percentile1272
Maximum5644
Range5644
Interquartile range (IQR)707

Descriptive statistics

Standard deviation455.36928
Coefficient of variation (CV)1.0296067
Kurtosis11.293696
Mean442.27498
Median Absolute Deviation (MAD)381
Skewness1.7025821
Sum641741
Variance207361.18
MonotonicityNot monotonic
2024-05-03T04:25:52.722638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 464
32.0%
24 12
 
0.8%
16 9
 
0.6%
936 5
 
0.3%
20 5
 
0.3%
616 5
 
0.3%
662 5
 
0.3%
686 5
 
0.3%
560 4
 
0.3%
547 4
 
0.3%
Other values (623) 933
64.3%
ValueCountFrequency (%)
0 464
32.0%
2 1
 
0.1%
16 9
 
0.6%
20 5
 
0.3%
24 12
 
0.8%
25 1
 
0.1%
27 1
 
0.1%
28 3
 
0.2%
33 1
 
0.1%
35 1
 
0.1%
ValueCountFrequency (%)
5644 1
0.1%
2260 1
0.1%
2188 1
0.1%
2096 1
0.1%
1904 1
0.1%
1880 1
0.1%
1810 1
0.1%
1767 1
0.1%
1721 1
0.1%
1696 1
0.1%

BsmtFinType2
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Unf
1247 
Rec
 
54
LwQ
 
46
NoBsmt
 
38
BLQ
 
33
Other values (2)
 
33

Length

Max length6
Median length3
Mean length3.0785665
Min length3

Characters and Unicode

Total characters4467
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnf
2nd rowUnf
3rd rowUnf
4th rowUnf
5th rowUnf

Common Values

ValueCountFrequency (%)
Unf 1247
85.9%
Rec 54
 
3.7%
LwQ 46
 
3.2%
NoBsmt 38
 
2.6%
BLQ 33
 
2.3%
ALQ 19
 
1.3%
GLQ 14
 
1.0%

Length

2024-05-03T04:25:52.984011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:53.274651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
unf 1247
85.9%
rec 54
 
3.7%
lwq 46
 
3.2%
nobsmt 38
 
2.6%
blq 33
 
2.3%
alq 19
 
1.3%
glq 14
 
1.0%

Most occurring characters

ValueCountFrequency (%)
U 1247
27.9%
n 1247
27.9%
f 1247
27.9%
Q 112
 
2.5%
L 112
 
2.5%
B 71
 
1.6%
e 54
 
1.2%
c 54
 
1.2%
R 54
 
1.2%
w 46
 
1.0%
Other values (7) 223
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4467
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
U 1247
27.9%
n 1247
27.9%
f 1247
27.9%
Q 112
 
2.5%
L 112
 
2.5%
B 71
 
1.6%
e 54
 
1.2%
c 54
 
1.2%
R 54
 
1.2%
w 46
 
1.0%
Other values (7) 223
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4467
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
U 1247
27.9%
n 1247
27.9%
f 1247
27.9%
Q 112
 
2.5%
L 112
 
2.5%
B 71
 
1.6%
e 54
 
1.2%
c 54
 
1.2%
R 54
 
1.2%
w 46
 
1.0%
Other values (7) 223
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4467
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
U 1247
27.9%
n 1247
27.9%
f 1247
27.9%
Q 112
 
2.5%
L 112
 
2.5%
B 71
 
1.6%
e 54
 
1.2%
c 54
 
1.2%
R 54
 
1.2%
w 46
 
1.0%
Other values (7) 223
 
5.0%

BsmtFinSF2
Real number (ℝ)

ZEROS 

Distinct144
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.838043
Minimum0
Maximum1474
Zeros1284
Zeros (%)88.5%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:25:53.525650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile398
Maximum1474
Range1474
Interquartile range (IQR)0

Descriptive statistics

Standard deviation161.77732
Coefficient of variation (CV)3.4539728
Kurtosis19.964623
Mean46.838043
Median Absolute Deviation (MAD)0
Skewness4.2402296
Sum67962
Variance26171.903
MonotonicityNot monotonic
2024-05-03T04:25:53.808370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1284
88.5%
180 5
 
0.3%
374 3
 
0.2%
290 2
 
0.1%
469 2
 
0.1%
279 2
 
0.1%
551 2
 
0.1%
96 2
 
0.1%
480 2
 
0.1%
182 2
 
0.1%
Other values (134) 145
 
10.0%
ValueCountFrequency (%)
0 1284
88.5%
28 1
 
0.1%
32 1
 
0.1%
35 1
 
0.1%
40 1
 
0.1%
41 2
 
0.1%
64 2
 
0.1%
68 1
 
0.1%
80 1
 
0.1%
81 1
 
0.1%
ValueCountFrequency (%)
1474 1
0.1%
1127 1
0.1%
1120 1
0.1%
1085 1
0.1%
1080 1
0.1%
1063 1
0.1%
1061 1
0.1%
1057 1
0.1%
1031 1
0.1%
1029 1
0.1%

BsmtUnfSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct777
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.19711
Minimum0
Maximum2336
Zeros118
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:25:54.107621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1222
median479
Q3808
95-th percentile1468
Maximum2336
Range2336
Interquartile range (IQR)586

Descriptive statistics

Standard deviation442.20919
Coefficient of variation (CV)0.77963937
Kurtosis0.47511904
Mean567.19711
Median Absolute Deviation (MAD)289
Skewness0.91997674
Sum823003
Variance195548.97
MonotonicityNot monotonic
2024-05-03T04:25:54.398395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118
 
8.1%
728 9
 
0.6%
572 7
 
0.5%
300 7
 
0.5%
384 7
 
0.5%
600 7
 
0.5%
672 6
 
0.4%
280 6
 
0.4%
625 6
 
0.4%
270 6
 
0.4%
Other values (767) 1272
87.7%
ValueCountFrequency (%)
0 118
8.1%
14 1
 
0.1%
15 1
 
0.1%
23 2
 
0.1%
26 1
 
0.1%
29 1
 
0.1%
30 1
 
0.1%
32 2
 
0.1%
35 1
 
0.1%
36 4
 
0.3%
ValueCountFrequency (%)
2336 1
0.1%
2153 1
0.1%
2121 1
0.1%
2046 1
0.1%
2042 1
0.1%
2002 1
0.1%
1969 1
0.1%
1935 1
0.1%
1926 1
0.1%
1907 1
0.1%

Heating
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
GasA
1419 
Other
 
32

Length

Max length5
Median length4
Mean length4.0220538
Min length4

Characters and Unicode

Total characters5836
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGasA
2nd rowGasA
3rd rowGasA
4th rowGasA
5th rowGasA

Common Values

ValueCountFrequency (%)
GasA 1419
97.8%
Other 32
 
2.2%

Length

2024-05-03T04:25:54.639058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:54.882704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
gasa 1419
97.8%
other 32
 
2.2%

Most occurring characters

ValueCountFrequency (%)
G 1419
24.3%
a 1419
24.3%
s 1419
24.3%
A 1419
24.3%
O 32
 
0.5%
t 32
 
0.5%
h 32
 
0.5%
e 32
 
0.5%
r 32
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5836
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 1419
24.3%
a 1419
24.3%
s 1419
24.3%
A 1419
24.3%
O 32
 
0.5%
t 32
 
0.5%
h 32
 
0.5%
e 32
 
0.5%
r 32
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5836
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 1419
24.3%
a 1419
24.3%
s 1419
24.3%
A 1419
24.3%
O 32
 
0.5%
t 32
 
0.5%
h 32
 
0.5%
e 32
 
0.5%
r 32
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5836
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 1419
24.3%
a 1419
24.3%
s 1419
24.3%
A 1419
24.3%
O 32
 
0.5%
t 32
 
0.5%
h 32
 
0.5%
e 32
 
0.5%
r 32
 
0.5%

HeatingQC
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Ex
734 
TA
427 
Gd
240 
Fa
 
49
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2902
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowEx
2nd rowEx
3rd rowEx
4th rowGd
5th rowEx

Common Values

ValueCountFrequency (%)
Ex 734
50.6%
TA 427
29.4%
Gd 240
 
16.5%
Fa 49
 
3.4%
Po 1
 
0.1%

Length

2024-05-03T04:25:55.084582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:55.350580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ex 734
50.6%
ta 427
29.4%
gd 240
 
16.5%
fa 49
 
3.4%
po 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E 734
25.3%
x 734
25.3%
T 427
14.7%
A 427
14.7%
G 240
 
8.3%
d 240
 
8.3%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2902
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 734
25.3%
x 734
25.3%
T 427
14.7%
A 427
14.7%
G 240
 
8.3%
d 240
 
8.3%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2902
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 734
25.3%
x 734
25.3%
T 427
14.7%
A 427
14.7%
G 240
 
8.3%
d 240
 
8.3%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2902
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 734
25.3%
x 734
25.3%
T 427
14.7%
A 427
14.7%
G 240
 
8.3%
d 240
 
8.3%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

CentralAir
Boolean

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
True
1356 
False
 
95
ValueCountFrequency (%)
True 1356
93.5%
False 95
 
6.5%
2024-05-03T04:25:55.590445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Electrical
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
SBrkr
1326 
FuseA
 
94
Other
 
31

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters7255
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSBrkr
2nd rowSBrkr
3rd rowSBrkr
4th rowSBrkr
5th rowSBrkr

Common Values

ValueCountFrequency (%)
SBrkr 1326
91.4%
FuseA 94
 
6.5%
Other 31
 
2.1%

Length

2024-05-03T04:25:55.774888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:56.045352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
sbrkr 1326
91.4%
fusea 94
 
6.5%
other 31
 
2.1%

Most occurring characters

ValueCountFrequency (%)
r 2683
37.0%
S 1326
18.3%
B 1326
18.3%
k 1326
18.3%
e 125
 
1.7%
F 94
 
1.3%
u 94
 
1.3%
s 94
 
1.3%
A 94
 
1.3%
O 31
 
0.4%
Other values (2) 62
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7255
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 2683
37.0%
S 1326
18.3%
B 1326
18.3%
k 1326
18.3%
e 125
 
1.7%
F 94
 
1.3%
u 94
 
1.3%
s 94
 
1.3%
A 94
 
1.3%
O 31
 
0.4%
Other values (2) 62
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7255
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 2683
37.0%
S 1326
18.3%
B 1326
18.3%
k 1326
18.3%
e 125
 
1.7%
F 94
 
1.3%
u 94
 
1.3%
s 94
 
1.3%
A 94
 
1.3%
O 31
 
0.4%
Other values (2) 62
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7255
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 2683
37.0%
S 1326
18.3%
B 1326
18.3%
k 1326
18.3%
e 125
 
1.7%
F 94
 
1.3%
u 94
 
1.3%
s 94
 
1.3%
A 94
 
1.3%
O 31
 
0.4%
Other values (2) 62
 
0.9%

1stFlrSF
Real number (ℝ)

HIGH CORRELATION 

Distinct748
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1161.5513
Minimum334
Maximum4692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:25:56.266334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile672.5
Q1882
median1086
Q31391
95-th percentile1827
Maximum4692
Range4358
Interquartile range (IQR)509

Descriptive statistics

Standard deviation385.00253
Coefficient of variation (CV)0.33145546
Kurtosis5.8325354
Mean1161.5513
Median Absolute Deviation (MAD)234
Skewness1.3731415
Sum1685411
Variance148226.95
MonotonicityNot monotonic
2024-05-03T04:25:56.546555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 25
 
1.7%
1040 16
 
1.1%
912 14
 
1.0%
848 12
 
0.8%
894 12
 
0.8%
672 11
 
0.8%
816 9
 
0.6%
630 9
 
0.6%
936 7
 
0.5%
832 7
 
0.5%
Other values (738) 1329
91.6%
ValueCountFrequency (%)
334 1
 
0.1%
372 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
483 7
0.5%
495 1
 
0.1%
520 5
0.3%
525 1
 
0.1%
526 1
 
0.1%
536 1
 
0.1%
ValueCountFrequency (%)
4692 1
0.1%
3228 1
0.1%
3138 1
0.1%
2898 1
0.1%
2633 1
0.1%
2524 1
0.1%
2444 1
0.1%
2411 1
0.1%
2402 1
0.1%
2392 1
0.1%

2ndFlrSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct414
Distinct (%)28.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346.74225
Minimum0
Maximum2065
Zeros824
Zeros (%)56.8%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:25:56.803962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3728
95-th percentile1141.5
Maximum2065
Range2065
Interquartile range (IQR)728

Descriptive statistics

Standard deviation436.45328
Coefficient of variation (CV)1.2587254
Kurtosis-0.54527978
Mean346.74225
Median Absolute Deviation (MAD)0
Skewness0.81576634
Sum503123
Variance190491.46
MonotonicityNot monotonic
2024-05-03T04:25:57.064270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 824
56.8%
728 10
 
0.7%
504 9
 
0.6%
672 8
 
0.6%
546 8
 
0.6%
600 7
 
0.5%
720 7
 
0.5%
896 6
 
0.4%
689 5
 
0.3%
756 5
 
0.3%
Other values (404) 562
38.7%
ValueCountFrequency (%)
0 824
56.8%
110 1
 
0.1%
167 1
 
0.1%
192 1
 
0.1%
208 1
 
0.1%
213 1
 
0.1%
220 1
 
0.1%
224 1
 
0.1%
240 2
 
0.1%
252 2
 
0.1%
ValueCountFrequency (%)
2065 1
0.1%
1872 1
0.1%
1818 1
0.1%
1796 1
0.1%
1611 1
0.1%
1589 1
0.1%
1540 1
0.1%
1538 1
0.1%
1523 1
0.1%
1519 1
0.1%

LowQualFinSF
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8807719
Minimum0
Maximum572
Zeros1425
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:25:57.301224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum572
Range572
Interquartile range (IQR)0

Descriptive statistics

Standard deviation48.77156
Coefficient of variation (CV)8.2933943
Kurtosis82.693569
Mean5.8807719
Median Absolute Deviation (MAD)0
Skewness8.9825673
Sum8533
Variance2378.6651
MonotonicityNot monotonic
2024-05-03T04:25:57.531529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1425
98.2%
80 3
 
0.2%
360 2
 
0.1%
513 1
 
0.1%
528 1
 
0.1%
572 1
 
0.1%
144 1
 
0.1%
234 1
 
0.1%
392 1
 
0.1%
371 1
 
0.1%
Other values (14) 14
 
1.0%
ValueCountFrequency (%)
0 1425
98.2%
53 1
 
0.1%
80 3
 
0.2%
120 1
 
0.1%
144 1
 
0.1%
156 1
 
0.1%
205 1
 
0.1%
232 1
 
0.1%
234 1
 
0.1%
360 2
 
0.1%
ValueCountFrequency (%)
572 1
0.1%
528 1
0.1%
515 1
0.1%
514 1
0.1%
513 1
0.1%
481 1
0.1%
479 1
0.1%
473 1
0.1%
420 1
0.1%
397 1
0.1%

GrLivArea
Real number (ℝ)

HIGH CORRELATION 

Distinct858
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1514.1744
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:25:57.792085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile848
Q11128
median1464
Q31776
95-th percentile2464
Maximum5642
Range5308
Interquartile range (IQR)648

Descriptive statistics

Standard deviation525.79952
Coefficient of variation (CV)0.34725163
Kurtosis4.9274358
Mean1514.1744
Median Absolute Deviation (MAD)326
Skewness1.3735124
Sum2197067
Variance276465.14
MonotonicityNot monotonic
2024-05-03T04:25:58.065350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 22
 
1.5%
1040 14
 
1.0%
894 11
 
0.8%
848 10
 
0.7%
1456 10
 
0.7%
912 9
 
0.6%
816 8
 
0.6%
1200 8
 
0.6%
1092 8
 
0.6%
987 7
 
0.5%
Other values (848) 1344
92.6%
ValueCountFrequency (%)
334 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
520 1
 
0.1%
605 1
 
0.1%
616 1
 
0.1%
630 6
0.4%
672 2
 
0.1%
691 1
 
0.1%
693 1
 
0.1%
ValueCountFrequency (%)
5642 1
0.1%
4676 1
0.1%
4476 1
0.1%
4316 1
0.1%
3627 1
0.1%
3608 1
0.1%
3493 1
0.1%
3447 1
0.1%
3395 1
0.1%
3279 1
0.1%

BsmtFullBath
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
0
853 
1
582 
2
 
15
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1451
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 853
58.8%
1 582
40.1%
2 15
 
1.0%
3 1
 
0.1%

Length

2024-05-03T04:25:58.315798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:58.568619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 853
58.8%
1 582
40.1%
2 15
 
1.0%
3 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 853
58.8%
1 582
40.1%
2 15
 
1.0%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 853
58.8%
1 582
40.1%
2 15
 
1.0%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 853
58.8%
1 582
40.1%
2 15
 
1.0%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 853
58.8%
1 582
40.1%
2 15
 
1.0%
3 1
 
0.1%

BsmtHalfBath
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
0
1369 
1
 
80
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1451
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1369
94.3%
1 80
 
5.5%
2 2
 
0.1%

Length

2024-05-03T04:25:58.779814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:59.016543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1369
94.3%
1 80
 
5.5%
2 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1369
94.3%
1 80
 
5.5%
2 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1369
94.3%
1 80
 
5.5%
2 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1369
94.3%
1 80
 
5.5%
2 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1369
94.3%
1 80
 
5.5%
2 2
 
0.1%

FullBath
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
2
761 
1
649 
3
 
32
0
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1451
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 761
52.4%
1 649
44.7%
3 32
 
2.2%
0 9
 
0.6%

Length

2024-05-03T04:25:59.203001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:59.463604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 761
52.4%
1 649
44.7%
3 32
 
2.2%
0 9
 
0.6%

Most occurring characters

ValueCountFrequency (%)
2 761
52.4%
1 649
44.7%
3 32
 
2.2%
0 9
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 761
52.4%
1 649
44.7%
3 32
 
2.2%
0 9
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 761
52.4%
1 649
44.7%
3 32
 
2.2%
0 9
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 761
52.4%
1 649
44.7%
3 32
 
2.2%
0 9
 
0.6%

HalfBath
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
0
910 
1
529 
2
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1451
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 910
62.7%
1 529
36.5%
2 12
 
0.8%

Length

2024-05-03T04:25:59.668787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:25:59.904477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 910
62.7%
1 529
36.5%
2 12
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 910
62.7%
1 529
36.5%
2 12
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 910
62.7%
1 529
36.5%
2 12
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 910
62.7%
1 529
36.5%
2 12
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 910
62.7%
1 529
36.5%
2 12
 
0.8%

BedroomAbvGr
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8669883
Minimum0
Maximum8
Zeros6
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:26:00.079348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.81508567
Coefficient of variation (CV)0.28430031
Kurtosis2.2548207
Mean2.8669883
Median Absolute Deviation (MAD)0
Skewness0.21784688
Sum4160
Variance0.66436465
MonotonicityNot monotonic
2024-05-03T04:26:00.273344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 800
55.1%
2 356
24.5%
4 211
 
14.5%
1 49
 
3.4%
5 21
 
1.4%
6 7
 
0.5%
0 6
 
0.4%
8 1
 
0.1%
ValueCountFrequency (%)
0 6
 
0.4%
1 49
 
3.4%
2 356
24.5%
3 800
55.1%
4 211
 
14.5%
5 21
 
1.4%
6 7
 
0.5%
8 1
 
0.1%
ValueCountFrequency (%)
8 1
 
0.1%
6 7
 
0.5%
5 21
 
1.4%
4 211
 
14.5%
3 800
55.1%
2 356
24.5%
1 49
 
3.4%
0 6
 
0.4%

KitchenAbvGr
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
1
1384 
2
 
64
3
 
2
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1451
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1384
95.4%
2 64
 
4.4%
3 2
 
0.1%
0 1
 
0.1%

Length

2024-05-03T04:26:00.521475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:26:00.759855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 1384
95.4%
2 64
 
4.4%
3 2
 
0.1%
0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 1384
95.4%
2 64
 
4.4%
3 2
 
0.1%
0 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1384
95.4%
2 64
 
4.4%
3 2
 
0.1%
0 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1384
95.4%
2 64
 
4.4%
3 2
 
0.1%
0 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1384
95.4%
2 64
 
4.4%
3 2
 
0.1%
0 1
 
0.1%

KitchenQual
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
TA
734 
Gd
579 
Ex
99 
Fa
 
39

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2902
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowGd
5th rowGd

Common Values

ValueCountFrequency (%)
TA 734
50.6%
Gd 579
39.9%
Ex 99
 
6.8%
Fa 39
 
2.7%

Length

2024-05-03T04:26:00.960083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:26:01.201053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ta 734
50.6%
gd 579
39.9%
ex 99
 
6.8%
fa 39
 
2.7%

Most occurring characters

ValueCountFrequency (%)
T 734
25.3%
A 734
25.3%
G 579
20.0%
d 579
20.0%
E 99
 
3.4%
x 99
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2902
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 734
25.3%
A 734
25.3%
G 579
20.0%
d 579
20.0%
E 99
 
3.4%
x 99
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2902
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 734
25.3%
A 734
25.3%
G 579
20.0%
d 579
20.0%
E 99
 
3.4%
x 99
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2902
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 734
25.3%
A 734
25.3%
G 579
20.0%
d 579
20.0%
E 99
 
3.4%
x 99
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

Functional
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Typ
1352 
Other
 
99

Length

Max length5
Median length3
Mean length3.1364576
Min length3

Characters and Unicode

Total characters4551
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTyp
2nd rowTyp
3rd rowTyp
4th rowTyp
5th rowTyp

Common Values

ValueCountFrequency (%)
Typ 1352
93.2%
Other 99
 
6.8%

Length

2024-05-03T04:26:01.429738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:26:01.811340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
typ 1352
93.2%
other 99
 
6.8%

Most occurring characters

ValueCountFrequency (%)
T 1352
29.7%
y 1352
29.7%
p 1352
29.7%
O 99
 
2.2%
t 99
 
2.2%
h 99
 
2.2%
e 99
 
2.2%
r 99
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4551
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1352
29.7%
y 1352
29.7%
p 1352
29.7%
O 99
 
2.2%
t 99
 
2.2%
h 99
 
2.2%
e 99
 
2.2%
r 99
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4551
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1352
29.7%
y 1352
29.7%
p 1352
29.7%
O 99
 
2.2%
t 99
 
2.2%
h 99
 
2.2%
e 99
 
2.2%
r 99
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4551
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1352
29.7%
y 1352
29.7%
p 1352
29.7%
O 99
 
2.2%
t 99
 
2.2%
h 99
 
2.2%
e 99
 
2.2%
r 99
 
2.2%

Fireplaces
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
0
685 
1
648 
2
113 
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1451
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 685
47.2%
1 648
44.7%
2 113
 
7.8%
3 5
 
0.3%

Length

2024-05-03T04:26:02.205820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:26:02.635252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 685
47.2%
1 648
44.7%
2 113
 
7.8%
3 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 685
47.2%
1 648
44.7%
2 113
 
7.8%
3 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 685
47.2%
1 648
44.7%
2 113
 
7.8%
3 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 685
47.2%
1 648
44.7%
2 113
 
7.8%
3 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 685
47.2%
1 648
44.7%
2 113
 
7.8%
3 5
 
0.3%

FireplaceQu
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
NoFireplace
685 
Gd
378 
TA
311 
Fa
 
33
Ex
 
24

Length

Max length11
Median length2
Mean length6.2487939
Min length2

Characters and Unicode

Total characters9067
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNoFireplace
2nd rowTA
3rd rowTA
4th rowGd
5th rowTA

Common Values

ValueCountFrequency (%)
NoFireplace 685
47.2%
Gd 378
26.1%
TA 311
21.4%
Fa 33
 
2.3%
Ex 24
 
1.7%
Po 20
 
1.4%

Length

2024-05-03T04:26:03.058985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:26:03.521390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nofireplace 685
47.2%
gd 378
26.1%
ta 311
21.4%
fa 33
 
2.3%
ex 24
 
1.7%
po 20
 
1.4%

Most occurring characters

ValueCountFrequency (%)
e 1370
15.1%
a 718
7.9%
F 718
7.9%
o 705
7.8%
N 685
7.6%
r 685
7.6%
i 685
7.6%
p 685
7.6%
l 685
7.6%
c 685
7.6%
Other values (7) 1446
15.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9067
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1370
15.1%
a 718
7.9%
F 718
7.9%
o 705
7.8%
N 685
7.6%
r 685
7.6%
i 685
7.6%
p 685
7.6%
l 685
7.6%
c 685
7.6%
Other values (7) 1446
15.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9067
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1370
15.1%
a 718
7.9%
F 718
7.9%
o 705
7.8%
N 685
7.6%
r 685
7.6%
i 685
7.6%
p 685
7.6%
l 685
7.6%
c 685
7.6%
Other values (7) 1446
15.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9067
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1370
15.1%
a 718
7.9%
F 718
7.9%
o 705
7.8%
N 685
7.6%
r 685
7.6%
i 685
7.6%
p 685
7.6%
l 685
7.6%
c 685
7.6%
Other values (7) 1446
15.9%

GarageType
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Attchd
863 
Detchd
387 
BuiltIn
 
86
NoGarage
 
81
Other
 
34

Length

Max length8
Median length6
Mean length6.1474845
Min length5

Characters and Unicode

Total characters8920
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAttchd
2nd rowAttchd
3rd rowAttchd
4th rowDetchd
5th rowAttchd

Common Values

ValueCountFrequency (%)
Attchd 863
59.5%
Detchd 387
26.7%
BuiltIn 86
 
5.9%
NoGarage 81
 
5.6%
Other 34
 
2.3%

Length

2024-05-03T04:26:03.920712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:26:04.287308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
attchd 863
59.5%
detchd 387
26.7%
builtin 86
 
5.9%
nogarage 81
 
5.6%
other 34
 
2.3%

Most occurring characters

ValueCountFrequency (%)
t 2233
25.0%
h 1284
14.4%
c 1250
14.0%
d 1250
14.0%
A 863
 
9.7%
e 502
 
5.6%
D 387
 
4.3%
a 162
 
1.8%
r 115
 
1.3%
i 86
 
1.0%
Other values (10) 788
 
8.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 2233
25.0%
h 1284
14.4%
c 1250
14.0%
d 1250
14.0%
A 863
 
9.7%
e 502
 
5.6%
D 387
 
4.3%
a 162
 
1.8%
r 115
 
1.3%
i 86
 
1.0%
Other values (10) 788
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 2233
25.0%
h 1284
14.4%
c 1250
14.0%
d 1250
14.0%
A 863
 
9.7%
e 502
 
5.6%
D 387
 
4.3%
a 162
 
1.8%
r 115
 
1.3%
i 86
 
1.0%
Other values (10) 788
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 2233
25.0%
h 1284
14.4%
c 1250
14.0%
d 1250
14.0%
A 863
 
9.7%
e 502
 
5.6%
D 387
 
4.3%
a 162
 
1.8%
r 115
 
1.3%
i 86
 
1.0%
Other values (10) 788
 
8.8%

GarageFinish
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Unf
605 
RFn
418 
Fin
347 
NoGarage
81 

Length

Max length8
Median length3
Mean length3.2791178
Min length3

Characters and Unicode

Total characters4758
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRFn
2nd rowRFn
3rd rowRFn
4th rowUnf
5th rowRFn

Common Values

ValueCountFrequency (%)
Unf 605
41.7%
RFn 418
28.8%
Fin 347
23.9%
NoGarage 81
 
5.6%

Length

2024-05-03T04:26:04.682025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:26:05.045434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
unf 605
41.7%
rfn 418
28.8%
fin 347
23.9%
nogarage 81
 
5.6%

Most occurring characters

ValueCountFrequency (%)
n 1370
28.8%
F 765
16.1%
U 605
12.7%
f 605
12.7%
R 418
 
8.8%
i 347
 
7.3%
a 162
 
3.4%
o 81
 
1.7%
N 81
 
1.7%
G 81
 
1.7%
Other values (3) 243
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4758
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1370
28.8%
F 765
16.1%
U 605
12.7%
f 605
12.7%
R 418
 
8.8%
i 347
 
7.3%
a 162
 
3.4%
o 81
 
1.7%
N 81
 
1.7%
G 81
 
1.7%
Other values (3) 243
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4758
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1370
28.8%
F 765
16.1%
U 605
12.7%
f 605
12.7%
R 418
 
8.8%
i 347
 
7.3%
a 162
 
3.4%
o 81
 
1.7%
N 81
 
1.7%
G 81
 
1.7%
Other values (3) 243
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4758
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1370
28.8%
F 765
16.1%
U 605
12.7%
f 605
12.7%
R 418
 
8.8%
i 347
 
7.3%
a 162
 
3.4%
o 81
 
1.7%
N 81
 
1.7%
G 81
 
1.7%
Other values (3) 243
 
5.1%

GarageArea
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct438
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean472.52516
Minimum0
Maximum1418
Zeros81
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:26:05.287571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1327.5
median478
Q3576
95-th percentile849
Maximum1418
Range1418
Interquartile range (IQR)248.5

Descriptive statistics

Standard deviation214.17175
Coefficient of variation (CV)0.45324942
Kurtosis0.90926461
Mean472.52516
Median Absolute Deviation (MAD)118
Skewness0.18258498
Sum685634
Variance45869.539
MonotonicityNot monotonic
2024-05-03T04:26:05.552639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 81
 
5.6%
440 48
 
3.3%
576 47
 
3.2%
240 38
 
2.6%
484 33
 
2.3%
528 33
 
2.3%
288 27
 
1.9%
400 24
 
1.7%
264 24
 
1.7%
480 23
 
1.6%
Other values (428) 1073
73.9%
ValueCountFrequency (%)
0 81
5.6%
160 2
 
0.1%
164 1
 
0.1%
180 9
 
0.6%
186 1
 
0.1%
189 1
 
0.1%
192 1
 
0.1%
198 1
 
0.1%
200 4
 
0.3%
205 3
 
0.2%
ValueCountFrequency (%)
1418 1
0.1%
1390 1
0.1%
1356 1
0.1%
1248 1
0.1%
1220 1
0.1%
1166 1
0.1%
1134 1
0.1%
1069 1
0.1%
1053 1
0.1%
1052 2
0.1%

GarageQual
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
TA
1302 
NoGarage
 
81
Other
 
68

Length

Max length8
Median length2
Mean length2.4755341
Min length2

Characters and Unicode

Total characters3592
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1302
89.7%
NoGarage 81
 
5.6%
Other 68
 
4.7%

Length

2024-05-03T04:26:05.831321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:26:06.072401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ta 1302
89.7%
nogarage 81
 
5.6%
other 68
 
4.7%

Most occurring characters

ValueCountFrequency (%)
T 1302
36.2%
A 1302
36.2%
a 162
 
4.5%
r 149
 
4.1%
e 149
 
4.1%
N 81
 
2.3%
o 81
 
2.3%
G 81
 
2.3%
g 81
 
2.3%
O 68
 
1.9%
Other values (2) 136
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3592
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1302
36.2%
A 1302
36.2%
a 162
 
4.5%
r 149
 
4.1%
e 149
 
4.1%
N 81
 
2.3%
o 81
 
2.3%
G 81
 
2.3%
g 81
 
2.3%
O 68
 
1.9%
Other values (2) 136
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3592
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1302
36.2%
A 1302
36.2%
a 162
 
4.5%
r 149
 
4.1%
e 149
 
4.1%
N 81
 
2.3%
o 81
 
2.3%
G 81
 
2.3%
g 81
 
2.3%
O 68
 
1.9%
Other values (2) 136
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3592
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1302
36.2%
A 1302
36.2%
a 162
 
4.5%
r 149
 
4.1%
e 149
 
4.1%
N 81
 
2.3%
o 81
 
2.3%
G 81
 
2.3%
g 81
 
2.3%
O 68
 
1.9%
Other values (2) 136
 
3.8%

GarageCond
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
TA
1317 
NoGarage
 
81
Other
 
53

Length

Max length8
Median length2
Mean length2.444521
Min length2

Characters and Unicode

Total characters3547
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1317
90.8%
NoGarage 81
 
5.6%
Other 53
 
3.7%

Length

2024-05-03T04:26:06.282678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:26:06.547158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ta 1317
90.8%
nogarage 81
 
5.6%
other 53
 
3.7%

Most occurring characters

ValueCountFrequency (%)
T 1317
37.1%
A 1317
37.1%
a 162
 
4.6%
r 134
 
3.8%
e 134
 
3.8%
N 81
 
2.3%
o 81
 
2.3%
G 81
 
2.3%
g 81
 
2.3%
O 53
 
1.5%
Other values (2) 106
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3547
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
T 1317
37.1%
A 1317
37.1%
a 162
 
4.6%
r 134
 
3.8%
e 134
 
3.8%
N 81
 
2.3%
o 81
 
2.3%
G 81
 
2.3%
g 81
 
2.3%
O 53
 
1.5%
Other values (2) 106
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3547
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
T 1317
37.1%
A 1317
37.1%
a 162
 
4.6%
r 134
 
3.8%
e 134
 
3.8%
N 81
 
2.3%
o 81
 
2.3%
G 81
 
2.3%
g 81
 
2.3%
O 53
 
1.5%
Other values (2) 106
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3547
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
T 1317
37.1%
A 1317
37.1%
a 162
 
4.6%
r 134
 
3.8%
e 134
 
3.8%
N 81
 
2.3%
o 81
 
2.3%
G 81
 
2.3%
g 81
 
2.3%
O 53
 
1.5%
Other values (2) 106
 
3.0%

PavedDrive
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Y
1331 
N
 
90
P
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1451
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y 1331
91.7%
N 90
 
6.2%
P 30
 
2.1%

Length

2024-05-03T04:26:06.774089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:26:07.003837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
y 1331
91.7%
n 90
 
6.2%
p 30
 
2.1%

Most occurring characters

ValueCountFrequency (%)
Y 1331
91.7%
N 90
 
6.2%
P 30
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 1331
91.7%
N 90
 
6.2%
P 30
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 1331
91.7%
N 90
 
6.2%
P 30
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 1331
91.7%
N 90
 
6.2%
P 30
 
2.1%

WoodDeckSF
Real number (ℝ)

ZEROS 

Distinct274
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.412819
Minimum0
Maximum857
Zeros755
Zeros (%)52.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:26:07.227986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3168
95-th percentile335
Maximum857
Range857
Interquartile range (IQR)168

Descriptive statistics

Standard deviation125.43685
Coefficient of variation (CV)1.3285998
Kurtosis2.9972593
Mean94.412819
Median Absolute Deviation (MAD)0
Skewness1.5418716
Sum136993
Variance15734.404
MonotonicityNot monotonic
2024-05-03T04:26:07.498676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 755
52.0%
192 38
 
2.6%
100 35
 
2.4%
144 33
 
2.3%
120 31
 
2.1%
168 28
 
1.9%
140 15
 
1.0%
224 14
 
1.0%
208 10
 
0.7%
240 10
 
0.7%
Other values (264) 482
33.2%
ValueCountFrequency (%)
0 755
52.0%
12 2
 
0.1%
24 2
 
0.1%
26 2
 
0.1%
28 2
 
0.1%
30 1
 
0.1%
32 1
 
0.1%
33 1
 
0.1%
35 1
 
0.1%
36 4
 
0.3%
ValueCountFrequency (%)
857 1
0.1%
736 1
0.1%
728 1
0.1%
670 1
0.1%
668 1
0.1%
635 1
0.1%
586 1
0.1%
576 1
0.1%
574 1
0.1%
550 1
0.1%

OpenPorchSF
Real number (ℝ)

ZEROS 

Distinct201
Distinct (%)13.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.422467
Minimum0
Maximum547
Zeros653
Zeros (%)45.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:26:07.771137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24
Q368
95-th percentile173
Maximum547
Range547
Interquartile range (IQR)68

Descriptive statistics

Standard deviation66.06015
Coefficient of variation (CV)1.4230211
Kurtosis8.6649934
Mean46.422467
Median Absolute Deviation (MAD)24
Skewness2.3849083
Sum67359
Variance4363.9435
MonotonicityNot monotonic
2024-05-03T04:26:08.024223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 653
45.0%
36 29
 
2.0%
20 21
 
1.4%
48 21
 
1.4%
45 19
 
1.3%
40 19
 
1.3%
30 16
 
1.1%
24 16
 
1.1%
60 15
 
1.0%
39 14
 
1.0%
Other values (191) 628
43.3%
ValueCountFrequency (%)
0 653
45.0%
4 1
 
0.1%
8 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
12 3
 
0.2%
15 1
 
0.1%
16 8
 
0.6%
17 2
 
0.1%
18 5
 
0.3%
ValueCountFrequency (%)
547 1
0.1%
523 1
0.1%
502 1
0.1%
418 1
0.1%
406 1
0.1%
364 1
0.1%
341 1
0.1%
319 1
0.1%
312 2
0.1%
304 1
0.1%

EnclosedPorch
Real number (ℝ)

ZEROS 

Distinct119
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.952447
Minimum0
Maximum552
Zeros1244
Zeros (%)85.7%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:26:08.300877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile180
Maximum552
Range552
Interquartile range (IQR)0

Descriptive statistics

Standard deviation61.108223
Coefficient of variation (CV)2.7836635
Kurtosis10.474485
Mean21.952447
Median Absolute Deviation (MAD)0
Skewness3.0940011
Sum31853
Variance3734.215
MonotonicityNot monotonic
2024-05-03T04:26:08.567359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1244
85.7%
112 15
 
1.0%
96 6
 
0.4%
120 5
 
0.3%
216 5
 
0.3%
192 5
 
0.3%
144 5
 
0.3%
252 4
 
0.3%
116 4
 
0.3%
156 4
 
0.3%
Other values (109) 154
 
10.6%
ValueCountFrequency (%)
0 1244
85.7%
19 1
 
0.1%
20 1
 
0.1%
24 1
 
0.1%
30 1
 
0.1%
32 2
 
0.1%
34 2
 
0.1%
36 2
 
0.1%
37 1
 
0.1%
39 2
 
0.1%
ValueCountFrequency (%)
552 1
0.1%
386 1
0.1%
330 1
0.1%
318 1
0.1%
301 1
0.1%
294 1
0.1%
293 1
0.1%
291 1
0.1%
286 1
0.1%
280 1
0.1%

3SsnPorch
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4307374
Minimum0
Maximum508
Zeros1427
Zeros (%)98.3%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:26:08.835638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum508
Range508
Interquartile range (IQR)0

Descriptive statistics

Standard deviation29.40694
Coefficient of variation (CV)8.5716091
Kurtosis122.87569
Mean3.4307374
Median Absolute Deviation (MAD)0
Skewness10.271742
Sum4978
Variance864.76813
MonotonicityNot monotonic
2024-05-03T04:26:09.053646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 1427
98.3%
168 3
 
0.2%
180 2
 
0.1%
144 2
 
0.1%
216 2
 
0.1%
130 1
 
0.1%
320 1
 
0.1%
407 1
 
0.1%
508 1
 
0.1%
238 1
 
0.1%
Other values (10) 10
 
0.7%
ValueCountFrequency (%)
0 1427
98.3%
23 1
 
0.1%
96 1
 
0.1%
130 1
 
0.1%
140 1
 
0.1%
144 2
 
0.1%
153 1
 
0.1%
162 1
 
0.1%
168 3
 
0.2%
180 2
 
0.1%
ValueCountFrequency (%)
508 1
0.1%
407 1
0.1%
320 1
0.1%
304 1
0.1%
290 1
0.1%
245 1
0.1%
238 1
0.1%
216 2
0.1%
196 1
0.1%
182 1
0.1%

ScreenPorch
Real number (ℝ)

ZEROS 

Distinct76
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.154376
Minimum0
Maximum480
Zeros1335
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:26:09.308547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile160
Maximum480
Range480
Interquartile range (IQR)0

Descriptive statistics

Standard deviation55.917522
Coefficient of variation (CV)3.6898597
Kurtosis18.29903
Mean15.154376
Median Absolute Deviation (MAD)0
Skewness4.1074107
Sum21989
Variance3126.7693
MonotonicityNot monotonic
2024-05-03T04:26:09.597685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1335
92.0%
192 6
 
0.4%
120 5
 
0.3%
224 5
 
0.3%
189 4
 
0.3%
180 4
 
0.3%
168 3
 
0.2%
90 3
 
0.2%
144 3
 
0.2%
126 3
 
0.2%
Other values (66) 80
 
5.5%
ValueCountFrequency (%)
0 1335
92.0%
40 1
 
0.1%
53 1
 
0.1%
60 1
 
0.1%
63 1
 
0.1%
80 1
 
0.1%
90 3
 
0.2%
95 1
 
0.1%
99 1
 
0.1%
100 2
 
0.1%
ValueCountFrequency (%)
480 1
0.1%
440 1
0.1%
410 1
0.1%
396 1
0.1%
385 1
0.1%
374 1
0.1%
322 1
0.1%
312 1
0.1%
291 1
0.1%
288 2
0.1%

PoolArea
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7760165
Minimum0
Maximum738
Zeros1444
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:26:09.828809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum738
Range738
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.301212
Coefficient of variation (CV)14.517641
Kurtosis221.86639
Mean2.7760165
Median Absolute Deviation (MAD)0
Skewness14.782069
Sum4028
Variance1624.1877
MonotonicityNot monotonic
2024-05-03T04:26:10.038093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1444
99.5%
512 1
 
0.1%
648 1
 
0.1%
576 1
 
0.1%
555 1
 
0.1%
480 1
 
0.1%
519 1
 
0.1%
738 1
 
0.1%
ValueCountFrequency (%)
0 1444
99.5%
480 1
 
0.1%
512 1
 
0.1%
519 1
 
0.1%
555 1
 
0.1%
576 1
 
0.1%
648 1
 
0.1%
738 1
 
0.1%
ValueCountFrequency (%)
738 1
 
0.1%
648 1
 
0.1%
576 1
 
0.1%
555 1
 
0.1%
519 1
 
0.1%
512 1
 
0.1%
480 1
 
0.1%
0 1444
99.5%

PoolQC
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
NoPool
1444 
Other
 
7

Length

Max length6
Median length6
Mean length5.9951757
Min length5

Characters and Unicode

Total characters8699
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNoPool
2nd rowNoPool
3rd rowNoPool
4th rowNoPool
5th rowNoPool

Common Values

ValueCountFrequency (%)
NoPool 1444
99.5%
Other 7
 
0.5%

Length

2024-05-03T04:26:10.289435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:26:10.534393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nopool 1444
99.5%
other 7
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o 4332
49.8%
N 1444
 
16.6%
P 1444
 
16.6%
l 1444
 
16.6%
O 7
 
0.1%
t 7
 
0.1%
h 7
 
0.1%
e 7
 
0.1%
r 7
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8699
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 4332
49.8%
N 1444
 
16.6%
P 1444
 
16.6%
l 1444
 
16.6%
O 7
 
0.1%
t 7
 
0.1%
h 7
 
0.1%
e 7
 
0.1%
r 7
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8699
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 4332
49.8%
N 1444
 
16.6%
P 1444
 
16.6%
l 1444
 
16.6%
O 7
 
0.1%
t 7
 
0.1%
h 7
 
0.1%
e 7
 
0.1%
r 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8699
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 4332
49.8%
N 1444
 
16.6%
P 1444
 
16.6%
l 1444
 
16.6%
O 7
 
0.1%
t 7
 
0.1%
h 7
 
0.1%
e 7
 
0.1%
r 7
 
0.1%

Fence
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
NoFence
1170 
MnPrv
157 
Other
124 

Length

Max length7
Median length7
Mean length6.6126809
Min length5

Characters and Unicode

Total characters9595
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNoFence
2nd rowNoFence
3rd rowNoFence
4th rowNoFence
5th rowNoFence

Common Values

ValueCountFrequency (%)
NoFence 1170
80.6%
MnPrv 157
 
10.8%
Other 124
 
8.5%

Length

2024-05-03T04:26:10.753561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:26:11.023288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nofence 1170
80.6%
mnprv 157
 
10.8%
other 124
 
8.5%

Most occurring characters

ValueCountFrequency (%)
e 2464
25.7%
n 1327
13.8%
N 1170
12.2%
F 1170
12.2%
o 1170
12.2%
c 1170
12.2%
r 281
 
2.9%
M 157
 
1.6%
P 157
 
1.6%
v 157
 
1.6%
Other values (3) 372
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9595
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2464
25.7%
n 1327
13.8%
N 1170
12.2%
F 1170
12.2%
o 1170
12.2%
c 1170
12.2%
r 281
 
2.9%
M 157
 
1.6%
P 157
 
1.6%
v 157
 
1.6%
Other values (3) 372
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9595
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2464
25.7%
n 1327
13.8%
N 1170
12.2%
F 1170
12.2%
o 1170
12.2%
c 1170
12.2%
r 281
 
2.9%
M 157
 
1.6%
P 157
 
1.6%
v 157
 
1.6%
Other values (3) 372
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9595
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2464
25.7%
n 1327
13.8%
N 1170
12.2%
F 1170
12.2%
o 1170
12.2%
c 1170
12.2%
r 281
 
2.9%
M 157
 
1.6%
P 157
 
1.6%
v 157
 
1.6%
Other values (3) 372
 
3.9%

MiscFeature
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
NoMisc
1397 
Other
 
54

Length

Max length6
Median length6
Mean length5.9627843
Min length5

Characters and Unicode

Total characters8652
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNoMisc
2nd rowNoMisc
3rd rowNoMisc
4th rowNoMisc
5th rowNoMisc

Common Values

ValueCountFrequency (%)
NoMisc 1397
96.3%
Other 54
 
3.7%

Length

2024-05-03T04:26:11.228809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:26:11.460036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
nomisc 1397
96.3%
other 54
 
3.7%

Most occurring characters

ValueCountFrequency (%)
N 1397
16.1%
o 1397
16.1%
M 1397
16.1%
i 1397
16.1%
s 1397
16.1%
c 1397
16.1%
O 54
 
0.6%
t 54
 
0.6%
h 54
 
0.6%
e 54
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8652
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 1397
16.1%
o 1397
16.1%
M 1397
16.1%
i 1397
16.1%
s 1397
16.1%
c 1397
16.1%
O 54
 
0.6%
t 54
 
0.6%
h 54
 
0.6%
e 54
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8652
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 1397
16.1%
o 1397
16.1%
M 1397
16.1%
i 1397
16.1%
s 1397
16.1%
c 1397
16.1%
O 54
 
0.6%
t 54
 
0.6%
h 54
 
0.6%
e 54
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8652
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 1397
16.1%
o 1397
16.1%
M 1397
16.1%
i 1397
16.1%
s 1397
16.1%
c 1397
16.1%
O 54
 
0.6%
t 54
 
0.6%
h 54
 
0.6%
e 54
 
0.6%

MiscVal
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct21
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.758787
Minimum0
Maximum15500
Zeros1399
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:26:11.637087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15500
Range15500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation497.64847
Coefficient of variation (CV)11.372538
Kurtosis696.69209
Mean43.758787
Median Absolute Deviation (MAD)0
Skewness24.401513
Sum63494
Variance247654
MonotonicityNot monotonic
2024-05-03T04:26:11.864129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 1399
96.4%
400 11
 
0.8%
500 8
 
0.6%
700 5
 
0.3%
450 4
 
0.3%
600 4
 
0.3%
2000 4
 
0.3%
1200 2
 
0.1%
480 2
 
0.1%
350 1
 
0.1%
Other values (11) 11
 
0.8%
ValueCountFrequency (%)
0 1399
96.4%
54 1
 
0.1%
350 1
 
0.1%
400 11
 
0.8%
450 4
 
0.3%
480 2
 
0.1%
500 8
 
0.6%
560 1
 
0.1%
600 4
 
0.3%
620 1
 
0.1%
ValueCountFrequency (%)
15500 1
 
0.1%
8300 1
 
0.1%
3500 1
 
0.1%
2500 1
 
0.1%
2000 4
0.3%
1400 1
 
0.1%
1300 1
 
0.1%
1200 2
0.1%
1150 1
 
0.1%
800 1
 
0.1%

MoSold
Real number (ℝ)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3190903
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:26:12.120279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median6
Q38
95-th percentile11
Maximum12
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7003526
Coefficient of variation (CV)0.4273325
Kurtosis-0.3981785
Mean6.3190903
Median Absolute Deviation (MAD)2
Skewness0.20925736
Sum9169
Variance7.2919043
MonotonicityNot monotonic
2024-05-03T04:26:12.318546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 253
17.4%
7 234
16.1%
5 201
13.9%
4 141
9.7%
8 121
8.3%
3 104
7.2%
10 89
 
6.1%
11 78
 
5.4%
9 62
 
4.3%
12 58
 
4.0%
Other values (2) 110
7.6%
ValueCountFrequency (%)
1 58
 
4.0%
2 52
 
3.6%
3 104
7.2%
4 141
9.7%
5 201
13.9%
6 253
17.4%
7 234
16.1%
8 121
8.3%
9 62
 
4.3%
10 89
 
6.1%
ValueCountFrequency (%)
12 58
 
4.0%
11 78
 
5.4%
10 89
 
6.1%
9 62
 
4.3%
8 121
8.3%
7 234
16.1%
6 253
17.4%
5 201
13.9%
4 141
9.7%
3 104
7.2%

SaleType
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
WD
1261 
New
 
119
Other
 
71

Length

Max length5
Median length2
Mean length2.2288077
Min length2

Characters and Unicode

Total characters3234
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWD
2nd rowWD
3rd rowWD
4th rowWD
5th rowWD

Common Values

ValueCountFrequency (%)
WD 1261
86.9%
New 119
 
8.2%
Other 71
 
4.9%

Length

2024-05-03T04:26:12.554668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:26:12.806281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
wd 1261
86.9%
new 119
 
8.2%
other 71
 
4.9%

Most occurring characters

ValueCountFrequency (%)
W 1261
39.0%
D 1261
39.0%
e 190
 
5.9%
N 119
 
3.7%
w 119
 
3.7%
O 71
 
2.2%
t 71
 
2.2%
h 71
 
2.2%
r 71
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3234
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W 1261
39.0%
D 1261
39.0%
e 190
 
5.9%
N 119
 
3.7%
w 119
 
3.7%
O 71
 
2.2%
t 71
 
2.2%
h 71
 
2.2%
r 71
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3234
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W 1261
39.0%
D 1261
39.0%
e 190
 
5.9%
N 119
 
3.7%
w 119
 
3.7%
O 71
 
2.2%
t 71
 
2.2%
h 71
 
2.2%
r 71
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3234
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W 1261
39.0%
D 1261
39.0%
e 190
 
5.9%
N 119
 
3.7%
w 119
 
3.7%
O 71
 
2.2%
t 71
 
2.2%
h 71
 
2.2%
r 71
 
2.2%

SaleCondition
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Normal
1193 
Partial
122 
Abnorml
 
101
Other
 
35

Length

Max length7
Median length6
Mean length6.1295658
Min length5

Characters and Unicode

Total characters8894
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowNormal
4th rowAbnorml
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal 1193
82.2%
Partial 122
 
8.4%
Abnorml 101
 
7.0%
Other 35
 
2.4%

Length

2024-05-03T04:26:13.034394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:26:13.303592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
normal 1193
82.2%
partial 122
 
8.4%
abnorml 101
 
7.0%
other 35
 
2.4%

Most occurring characters

ValueCountFrequency (%)
r 1451
16.3%
a 1437
16.2%
l 1416
15.9%
m 1294
14.5%
o 1294
14.5%
N 1193
13.4%
t 157
 
1.8%
P 122
 
1.4%
i 122
 
1.4%
A 101
 
1.1%
Other values (5) 307
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8894
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1451
16.3%
a 1437
16.2%
l 1416
15.9%
m 1294
14.5%
o 1294
14.5%
N 1193
13.4%
t 157
 
1.8%
P 122
 
1.4%
i 122
 
1.4%
A 101
 
1.1%
Other values (5) 307
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8894
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1451
16.3%
a 1437
16.2%
l 1416
15.9%
m 1294
14.5%
o 1294
14.5%
N 1193
13.4%
t 157
 
1.8%
P 122
 
1.4%
i 122
 
1.4%
A 101
 
1.1%
Other values (5) 307
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8894
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1451
16.3%
a 1437
16.2%
l 1416
15.9%
m 1294
14.5%
o 1294
14.5%
N 1193
13.4%
t 157
 
1.8%
P 122
 
1.4%
i 122
 
1.4%
A 101
 
1.1%
Other values (5) 307
 
3.5%

SalePrice
Real number (ℝ)

HIGH CORRELATION 

Distinct657
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180624.1
Minimum34900
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:26:13.541394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum34900
5-th percentile88000
Q1129900
median162500
Q3214000
95-th percentile325812
Maximum755000
Range720100
Interquartile range (IQR)84100

Descriptive statistics

Standard deviation79312.128
Coefficient of variation (CV)0.43910047
Kurtosis6.573025
Mean180624.1
Median Absolute Deviation (MAD)37500
Skewness1.883111
Sum2.6208557 × 108
Variance6.2904137 × 109
MonotonicityNot monotonic
2024-05-03T04:26:13.819562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140000 20
 
1.4%
135000 17
 
1.2%
155000 14
 
1.0%
145000 14
 
1.0%
110000 13
 
0.9%
190000 13
 
0.9%
115000 12
 
0.8%
160000 12
 
0.8%
130000 11
 
0.8%
139000 11
 
0.8%
Other values (647) 1314
90.6%
ValueCountFrequency (%)
34900 1
0.1%
35311 1
0.1%
37900 1
0.1%
39300 1
0.1%
40000 1
0.1%
52000 1
0.1%
52500 1
0.1%
55000 2
0.1%
55993 1
0.1%
58500 1
0.1%
ValueCountFrequency (%)
755000 1
0.1%
745000 1
0.1%
625000 1
0.1%
611657 1
0.1%
582933 1
0.1%
556581 1
0.1%
555000 1
0.1%
538000 1
0.1%
501837 1
0.1%
485000 1
0.1%

House Category
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
Contemporary Family Home
460 
Vintage Family Home
406 
Modern Family Home
346 
Modern Townhouse
73 
Contemporary Townhouse
64 
Other values (4)
102 

Length

Max length35
Median length30
Mean length21.046175
Min length16

Characters and Unicode

Total characters30538
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModern Family Home
2nd rowContemporary Family Home
3rd rowContemporary Family Home
4th rowVintage Family Home
5th rowContemporary Family Home

Common Values

ValueCountFrequency (%)
Contemporary Family Home 460
31.7%
Vintage Family Home 406
28.0%
Modern Family Home 346
23.8%
Modern Townhouse 73
 
5.0%
Contemporary Townhouse 64
 
4.4%
Vintage Multi-Family or Duplex 46
 
3.2%
Contemporary Multi-Family or Duplex 34
 
2.3%
Vintage Townhouse 19
 
1.3%
Modern Multi-Family or Duplex 3
 
0.2%

Length

2024-05-03T04:26:14.097853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:26:14.382988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
family 1212
28.3%
home 1212
28.3%
contemporary 558
13.0%
vintage 471
 
11.0%
modern 422
 
9.9%
townhouse 156
 
3.6%
multi-family 83
 
1.9%
or 83
 
1.9%
duplex 83
 
1.9%

Most occurring characters

ValueCountFrequency (%)
o 3145
 
10.3%
m 3065
 
10.0%
e 2902
 
9.5%
2829
 
9.3%
a 2324
 
7.6%
y 1853
 
6.1%
i 1849
 
6.1%
r 1621
 
5.3%
n 1607
 
5.3%
l 1461
 
4.8%
Other values (17) 7882
25.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30538
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 3145
 
10.3%
m 3065
 
10.0%
e 2902
 
9.5%
2829
 
9.3%
a 2324
 
7.6%
y 1853
 
6.1%
i 1849
 
6.1%
r 1621
 
5.3%
n 1607
 
5.3%
l 1461
 
4.8%
Other values (17) 7882
25.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30538
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 3145
 
10.3%
m 3065
 
10.0%
e 2902
 
9.5%
2829
 
9.3%
a 2324
 
7.6%
y 1853
 
6.1%
i 1849
 
6.1%
r 1621
 
5.3%
n 1607
 
5.3%
l 1461
 
4.8%
Other values (17) 7882
25.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30538
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 3145
 
10.3%
m 3065
 
10.0%
e 2902
 
9.5%
2829
 
9.3%
a 2324
 
7.6%
y 1853
 
6.1%
i 1849
 
6.1%
r 1621
 
5.3%
n 1607
 
5.3%
l 1461
 
4.8%
Other values (17) 7882
25.8%

HasGarage
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
1
1370 
0
 
81

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1451
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1370
94.4%
0 81
 
5.6%

Length

2024-05-03T04:26:14.657906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:26:14.881789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 1370
94.4%
0 81
 
5.6%

Most occurring characters

ValueCountFrequency (%)
1 1370
94.4%
0 81
 
5.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1370
94.4%
0 81
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1370
94.4%
0 81
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1370
94.4%
0 81
 
5.6%

YearsSinceGarage
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct106
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139.95658
Minimum0
Maximum2011
Zeros82
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-03T04:26:15.218406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median30
Q350
95-th percentile2007
Maximum2011
Range2011
Interquartile range (IQR)43

Descriptive statistics

Standard deviation455.22732
Coefficient of variation (CV)3.2526324
Kurtosis12.933463
Mean139.95658
Median Absolute Deviation (MAD)22
Skewness3.8553617
Sum203077
Variance207231.91
MonotonicityNot monotonic
2024-05-03T04:26:15.667325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 84
 
5.8%
0 82
 
5.7%
4 45
 
3.1%
2 35
 
2.4%
3 34
 
2.3%
5 34
 
2.3%
31 31
 
2.1%
8 29
 
2.0%
6 29
 
2.0%
9 27
 
1.9%
Other values (96) 1021
70.4%
ValueCountFrequency (%)
0 82
5.7%
1 84
5.8%
2 35
2.4%
3 34
2.3%
4 45
3.1%
5 34
2.3%
6 29
 
2.0%
7 26
 
1.8%
8 29
 
2.0%
9 27
 
1.9%
ValueCountFrequency (%)
2011 11
0.8%
2010 15
1.0%
2009 23
1.6%
2008 17
1.2%
2007 15
1.0%
107 1
 
0.1%
102 1
 
0.1%
100 1
 
0.1%
99 1
 
0.1%
97 1
 
0.1%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.7 KiB
0
1194 
1
257 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1451
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1194
82.3%
1 257
 
17.7%

Length

2024-05-03T04:26:16.122821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-03T04:26:16.542310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1194
82.3%
1 257
 
17.7%

Most occurring characters

ValueCountFrequency (%)
0 1194
82.3%
1 257
 
17.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1194
82.3%
1 257
 
17.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1194
82.3%
1 257
 
17.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1451
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1194
82.3%
1 257
 
17.7%

Interactions

2024-05-03T04:25:26.469083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:22:48.491504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:22:54.684664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:01.701341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:08.635861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:15.181250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:21.690153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:28.602017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:34.971985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:41.888634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:48.228447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:55.907277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:01.512423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:08.335943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:14.689526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:21.811753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:27.826129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:34.797083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:40.565623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:47.392497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:54.014742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:00.777534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:06.702172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:13.744922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:19.379988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:26.735132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:22:48.714518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:22:55.064267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:01.920430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:09.003165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:15.415795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:22.054653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:28.824952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:35.296012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:42.120004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:48.545395image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:56.122249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:01.860763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:08.549643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:15.077542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:22.049173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:28.057143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:35.027909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:40.790082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:47.637088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:54.238107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:01.010313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:06.969222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:13.975148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:19.625921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:26.976474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:22:48.962587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:22:55.408895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:02.144511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:09.346545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:15.637161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:22.368646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:29.033923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:35.650918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:42.336881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:48.895412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:56.334249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:02.168498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:08.770332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:15.384464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:22.276627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:28.353781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:35.250540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:41.003563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:47.851281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:54.458167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:01.246810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:07.226892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:14.194898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:19.860262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:27.202859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:22:49.166195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:22:55.759152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:02.359648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:09.686433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:15.854896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:22.737189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:29.237739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:36.009609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:42.547812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:49.728194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:56.561179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:02.531338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:08.968858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:15.694297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:22.495536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:28.715796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:35.463701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:41.201164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:48.063489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:54.660972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:01.456766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:07.448316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:14.393530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:20.087545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:27.442937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:22:49.381228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:22:56.107510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:02.575003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:10.045281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:16.089316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:23.094422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:29.435397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:36.352831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:42.763367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:50.414561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:56.766962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:02.829296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:09.196325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:16.060348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:22.752106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:29.040042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:35.667819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:41.429655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:48.260478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:54.894621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:01.682817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:07.676818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:14.603637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:20.310113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:27.692585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-05-03T04:23:57.233028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2024-05-03T04:25:33.186270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:22:53.964113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:00.735117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:07.806413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:14.693745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:21.017116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:28.085417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:34.376470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:41.368634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:47.647330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:55.398671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:00.998865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:07.848179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:14.192996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:21.303529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:27.316180image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:34.316120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:40.069869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:46.904430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:53.527693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:00.319293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:06.191118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:13.256927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:18.904161image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:25.952386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:33.446362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:22:54.310692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:00.982552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:08.228699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:14.947336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:21.328860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:28.346445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:34.614569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:41.628099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:47.893294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:23:55.640051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:01.229167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:08.096529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:14.453836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:21.548551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:27.565302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:34.562800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:40.315862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:47.158206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:24:53.759229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:00.542559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:06.446785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:13.492106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:19.145556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-05-03T04:25:26.221315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-05-03T04:26:17.067548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
1stFlrSF2ndFlrSF3SsnPorchAlleyBedroomAbvGrBsmtCondBsmtExposureBsmtFinSF1BsmtFinSF2BsmtFinType1BsmtFinType2BsmtFullBathBsmtHalfBathBsmtQualBsmtUnfSFCentralAirCondition1Condition2ElectricalEnclosedPorchExterCondExterQualExterior1stExterior2ndFenceFireplaceQuFireplacesFoundationFullBathFunctionalGarageAreaGarageCondGarageFinishGarageQualGarageTypeGrLivAreaHalfBathHasGarageHeatingHeatingQCHouse CategoryKitchenAbvGrKitchenQualLandContourLandSlopeLotAreaLotConfigLotFrontageLotShapeLowQualFinSFMSSubClassMSZoningMasVnrAreaMasVnrTypeMiscFeatureMiscValMoSoldNeighborhoodOpenPorchSFOverallCondOverallQualPavedDrivePoolAreaPoolQCRoofMatlRoofStyleSaleConditionSalePriceSaleTypeScreenPorchStreetUtilitiesWoodDeckSFYearsSinceGaragefillna_LotFrontage
1stFlrSF1.000-0.2730.0610.1720.1440.0000.1480.3200.0680.0970.0000.1900.0000.2230.2230.1440.0860.1740.070-0.1330.0000.2700.1110.1090.0770.1880.3400.1230.2570.0620.4900.1240.2110.1200.1950.4960.1230.1420.0000.0920.1740.0290.2510.0960.0000.4420.0650.3860.207-0.039-0.2750.2320.3520.2130.023-0.0330.0510.1460.233-0.1640.4080.1040.0710.3830.3080.2310.1350.5770.1770.1080.0000.0000.222-0.2700.069
2ndFlrSF-0.2731.000-0.0230.2080.5100.0490.131-0.190-0.1020.1200.0160.1240.0000.1770.0620.0350.0780.0900.0540.0480.0310.2090.1250.1320.0710.1520.1650.1960.4120.0000.0990.0570.1820.0520.2850.6450.4490.0790.0490.1100.1210.0000.1830.0580.0000.1230.0750.0450.1490.0580.4880.2260.0640.1120.000-0.0050.0490.1600.228-0.0000.2920.1120.0620.2830.1390.1600.0500.2940.0550.0120.0000.0000.070-0.0910.070
3SsnPorch0.061-0.0231.0000.000-0.0190.0000.0000.047-0.0160.0000.0000.0000.0570.0000.0140.0000.0290.0000.000-0.0390.0000.0310.0000.0200.0440.0000.0000.0780.0000.0000.0370.0000.0240.0000.0000.0340.0000.0000.0000.0000.0000.0000.0000.0730.0640.0630.0680.0530.0160.022-0.0360.0000.0410.0000.1140.0050.0370.0000.0180.0320.0340.000-0.0090.0000.0560.0490.0000.0660.000-0.0380.0000.000-0.028-0.0300.000
Alley0.1720.2080.0001.000-0.0140.1090.125-0.166-0.0650.1650.0450.1020.0000.1400.0770.1780.0380.0000.1040.2100.0500.0590.2000.2030.0000.1130.1110.2480.0240.005-0.0820.1540.1710.1260.282-0.0090.0000.0340.1040.0930.1120.0000.0570.1100.000-0.1300.076-0.2190.1150.0940.1330.368-0.1230.1290.0000.012-0.0190.4570.0330.074-0.0630.221-0.0180.0000.0000.1080.011-0.1280.000-0.0430.0000.000-0.1290.1230.074
BedroomAbvGr0.1440.510-0.019-0.0141.0000.1020.101-0.0820.0100.1030.0340.2550.0290.0920.1570.1610.1080.0000.130-0.0010.0000.1710.1040.0880.0260.0880.1060.1060.4470.0000.1160.1250.1230.1430.1720.5430.2510.1620.1040.0190.2240.2330.1310.1110.1010.3390.0250.2930.0320.0210.0700.1680.1130.0530.0000.0130.0570.1320.105-0.0030.1260.0980.0720.0700.1650.0770.0990.2370.0000.0340.0000.0000.0550.0350.059
BsmtCond0.0000.0490.0000.1090.1021.0000.4950.152-0.0010.5070.4950.1060.0600.5260.1040.3150.0660.0390.211-0.0490.1870.1670.0850.0760.0000.0440.0500.4150.1420.1970.0710.1820.1230.1160.1070.0160.0940.1350.1850.1020.1450.1850.1370.0610.1330.0230.0000.1110.060-0.044-0.0380.0780.0990.0600.000-0.008-0.0230.1080.0580.0310.1130.1690.0230.0000.0560.0000.0770.1280.0840.0300.0000.0000.054-0.1050.046
BsmtExposure0.1480.1310.0000.1250.1010.4951.000-0.308-0.0520.5220.4910.2130.0470.5210.0290.2110.0930.0000.1590.1010.0420.1670.1210.1150.0410.0870.1380.4080.0970.122-0.2550.0900.1590.0890.134-0.0830.1050.0910.1510.0950.1810.1750.1510.1930.222-0.1770.059-0.1350.1060.069-0.0660.098-0.1620.1260.0240.036-0.0190.180-0.1010.056-0.2690.112-0.0460.0260.2410.0980.109-0.3230.120-0.0170.0870.000-0.2860.2780.007
BsmtFinSF10.320-0.1900.047-0.166-0.0820.152-0.3081.0000.0520.2730.0690.3960.0290.215-0.5760.1520.1000.2550.099-0.1520.0000.2060.0880.0850.0570.1270.2970.1460.1570.0200.2420.0790.1700.0990.1410.0540.0150.0810.0000.0590.0830.0000.2080.1390.0840.1720.0580.1520.206-0.079-0.1070.1400.2410.1660.0000.006-0.0170.1500.077-0.0070.1300.1080.0580.3960.2000.1560.1070.3000.1250.0730.0210.0000.180-0.1110.097
BsmtFinSF20.068-0.102-0.016-0.0650.010-0.001-0.0520.0521.0000.1730.4280.0890.0880.048-0.2720.0000.0000.0000.0000.0410.0000.0390.0740.0900.1220.0540.0800.1080.0350.126-0.0060.0000.0130.0000.000-0.0510.0000.0000.0300.0000.0390.0000.0390.0480.1410.0730.0310.0530.0560.001-0.0840.047-0.0620.0950.0370.030-0.0260.072-0.0680.101-0.1160.0000.0680.0750.1470.1370.021-0.0370.1030.0580.0480.1840.0690.1190.082
BsmtFinType10.0970.1200.0000.1650.1030.5070.5220.2730.1731.0000.4460.3420.0820.5760.5190.2590.0730.0470.1980.1250.0760.2970.1990.2000.1490.1240.1200.5310.2220.200-0.0550.1250.2220.1370.1810.1260.0980.1160.1630.2090.2300.1760.2810.0870.041-0.0470.066-0.0620.0650.0900.0800.164-0.0860.2010.077-0.0580.0400.2400.024-0.0950.0730.192-0.0390.0000.0150.0440.140-0.0700.164-0.0390.0000.000-0.094-0.0290.098
BsmtFinType20.0000.0160.0000.0450.0340.4950.4910.0690.4280.4461.0000.1030.0910.5000.3620.1980.0410.0000.132-0.0310.0070.1120.0940.0940.1140.0490.0570.4130.0400.1530.0350.0620.0850.0690.0720.0810.0920.0970.1420.1010.1320.1640.0870.0000.075-0.0470.000-0.0280.065-0.0080.0650.0750.0900.0720.028-0.0510.0290.1260.110-0.0620.1950.094-0.0590.1450.0470.0640.0660.1220.066-0.0270.1040.117-0.019-0.1580.069
BsmtFullBath0.1900.1240.0000.1020.2550.1060.2130.3960.0890.3420.1031.0000.0960.124-0.4480.1060.0540.0000.079-0.0750.0000.0680.0920.0790.0000.0540.1110.1070.2650.0000.2010.0940.1130.1090.1250.0060.1520.1260.0540.0580.1860.1320.0940.1050.2000.0960.0000.0790.094-0.061-0.0410.0930.1210.0850.034-0.006-0.0270.1230.082-0.0490.0950.0850.0700.0950.1870.0930.1360.2230.0000.0310.0910.0000.170-0.1030.054
BsmtHalfBath0.0000.0000.0570.0000.0290.0600.0470.0290.0880.0820.0910.0961.0000.053-0.0980.0170.0000.0000.000-0.0370.0590.0420.0390.0370.0000.0340.0000.0760.1640.000-0.0220.0530.0610.0560.035-0.0260.1540.0760.0000.0290.0930.4980.0000.0260.0450.0460.0400.0010.045-0.0110.0020.0340.0330.0520.0000.0320.0370.104-0.0400.115-0.0500.0170.0260.0000.1350.0780.096-0.0110.0460.0380.0000.1020.0490.0710.019
BsmtQual0.2230.1770.0000.1400.0920.5260.5210.2150.0480.5760.5000.1240.0531.000-0.1240.2830.1320.1160.2310.1970.1040.4630.2630.2550.1850.2430.1830.5610.3420.175-0.4360.1970.3400.2070.237-0.3580.1730.1540.1660.2400.3560.1860.4190.0930.000-0.1310.073-0.1140.1430.083-0.1210.186-0.2660.2540.0760.054-0.0080.413-0.3430.305-0.5830.194-0.0170.0110.0000.1970.249-0.5700.3020.0290.0000.000-0.2680.6280.111
BsmtUnfSF0.2230.0620.0140.0770.1570.1040.029-0.576-0.2720.5190.362-0.448-0.098-0.1241.0000.0590.0000.0310.0640.0430.0260.2530.1350.1320.0930.1390.0890.2150.1880.1060.1090.0630.1320.1110.1060.2530.1210.0500.0000.0990.1390.0630.1930.0640.0530.0750.0120.0960.0410.020-0.1150.1050.0750.1680.000-0.0450.0370.1380.158-0.1290.2740.095-0.0370.0000.0120.1560.1720.1860.198-0.0120.0000.000-0.034-0.1840.090
CentralAir0.1440.0350.0000.1780.1610.3150.2110.1520.0000.2590.1980.1060.0170.2830.0591.0000.0530.0330.412-0.1900.2000.2780.2430.2440.0000.1970.1970.3580.1020.0730.2120.3370.3250.3060.3580.1030.1300.2440.4430.3780.3670.2470.3420.1280.0000.1060.0680.1200.108-0.048-0.1070.2040.1750.1790.000-0.0090.0150.2630.0910.1000.2480.3350.0180.0000.0000.0440.0870.3140.0800.0390.0400.0000.167-0.3160.063
Condition10.0860.0780.0290.0380.1080.0660.0930.1000.0000.0730.0410.0540.0000.1320.0000.0531.0000.2560.0580.0070.0580.1180.0680.0530.0580.0000.0370.1090.0480.0000.0260.0630.0980.0640.0870.0700.0730.0930.0370.0800.1440.0920.0920.0290.0000.0670.099-0.0130.064-0.0230.0260.048-0.0210.0390.000-0.0120.0050.1840.0340.0400.0800.0940.0230.0470.0560.0330.0000.0360.000-0.0040.0000.000-0.024-0.0210.000
Condition20.1740.0900.0000.0000.0000.0390.0000.2550.0000.0470.0000.0000.0000.1160.0310.0330.2561.0000.0000.0500.1410.1150.0130.0000.0000.0000.0000.1170.0650.0000.0240.0380.0000.1000.0680.0220.0700.0000.0000.0330.0730.1010.0710.0690.0000.0210.0210.0050.0750.0890.0480.062-0.0240.0000.0210.0550.0220.0560.0060.056-0.0260.027-0.0070.0000.0000.0000.000-0.0500.000-0.0300.0000.0000.0080.0230.000
Electrical0.0700.0540.0000.1040.1300.2110.1590.0990.0000.1980.1320.0790.0000.2310.0640.4120.0580.0001.000-0.1550.1470.1730.1890.2000.0190.1060.1130.1980.1460.0580.2500.1460.1830.1290.1790.1420.0890.1270.1960.1910.2410.1210.2360.0690.0000.0780.0420.0960.077-0.0340.0460.1060.1540.1090.0030.033-0.0040.1960.1480.0460.2480.1840.0210.0000.0000.0000.0770.2950.0610.0000.0000.0930.181-0.3240.031
EnclosedPorch-0.1330.048-0.0390.210-0.001-0.0490.101-0.1520.0410.125-0.031-0.075-0.0370.1970.043-0.1900.0070.050-0.1551.0000.0350.0880.1190.1180.0760.1030.0440.2340.1070.000-0.1780.1340.1200.1410.113-0.0510.0760.0940.1820.1270.0700.0260.0910.0270.000-0.0700.067-0.1000.0600.0480.0130.157-0.1800.0980.0000.039-0.0260.115-0.1670.113-0.1620.1940.0040.3720.0000.1250.089-0.2190.063-0.0810.0000.000-0.1580.3100.035
ExterCond0.0000.0310.0000.0500.0000.1870.0420.0000.0000.0760.0070.0000.0590.1040.0260.2000.0580.1410.1470.0351.0000.1810.0720.0750.0910.0120.0350.1430.0750.1480.1280.1690.1430.1560.1150.0610.0510.2150.0000.0620.0880.0000.1770.0000.0000.0330.0000.0800.000-0.0490.0130.0880.1080.0640.123-0.0180.0200.1060.060-0.2270.1280.154-0.0330.0000.0310.0740.0490.1270.083-0.0130.0000.0000.028-0.2120.027
ExterQual0.2700.2090.0310.0590.1710.1670.1670.2060.0390.2970.1120.0680.0420.4630.2530.2780.1180.1150.1730.0880.1811.0000.2930.2950.1910.2410.1880.3680.3190.131-0.4550.1930.3370.1850.281-0.4150.1500.2480.0670.3230.3700.0910.5440.1340.093-0.1300.019-0.1550.1140.015-0.0550.194-0.3170.2490.1070.047-0.0400.405-0.3810.261-0.6690.192-0.0320.0030.0000.1880.234-0.6310.2890.0300.3210.000-0.2340.6280.026
Exterior1st0.1110.1250.0000.2000.1040.0850.1210.0880.0740.1990.0940.0920.0390.2630.1350.2430.0680.0130.1890.1190.0720.2931.0000.8730.1610.1560.1260.3810.2320.1180.0770.1090.2540.1470.2100.1320.1290.1150.1680.2520.2810.0600.2570.1260.1430.0790.0920.0270.0950.018-0.0500.223-0.0420.2120.095-0.015-0.0000.3020.119-0.0890.1530.169-0.0120.0900.2210.1060.1860.1320.2120.0010.0320.0190.038-0.1380.175
Exterior2nd0.1090.1320.0200.2030.0880.0760.1150.0850.0900.2000.0940.0790.0370.2550.1320.2440.0530.0000.2000.1180.0750.2950.8731.0000.1620.1300.1120.3810.2190.1380.0780.0990.2470.1380.2100.1100.1230.0960.1760.2530.2770.0670.2600.1230.1260.0780.1000.0530.1030.008-0.0890.235-0.0460.2010.100-0.0250.0060.3040.101-0.0740.1410.155-0.0330.0910.1950.1090.1890.1300.2150.0130.0300.0260.029-0.1490.195
Fence0.0770.0710.0440.0000.0260.0000.0410.0570.1220.1490.1140.0000.0000.1850.0930.0000.0580.0000.0190.0760.0910.1910.1610.1621.0000.0920.0440.1820.1640.0730.0510.0210.1130.0000.0840.0670.0480.0000.0000.1480.1760.0410.1210.0530.0140.0590.0230.0260.043-0.0170.0250.0490.0550.0970.120-0.0260.0280.2010.034-0.0450.0730.0080.0480.1270.0210.0040.1080.0750.100-0.0080.0000.0000.019-0.0420.000
FireplaceQu0.1880.1520.0000.1130.0880.0440.0870.1270.0540.1240.0490.0540.0340.2430.1390.1970.0000.0000.1060.1030.0120.2410.1560.1300.0921.0000.5800.1540.1930.071-0.0550.1550.2360.1420.202-0.0530.1730.2000.0350.1160.2330.0790.2590.0750.032-0.0520.057-0.0230.122-0.0070.0730.168-0.0420.2000.0820.101-0.0520.240-0.007-0.000-0.1490.121-0.0210.1120.0890.1180.161-0.1170.191-0.0400.0000.000-0.0280.0870.174
Fireplaces0.3400.1650.0000.1110.1060.0500.1380.2970.0800.1200.0570.1110.0000.1830.0890.1970.0370.0000.1130.0440.0350.1880.1260.1120.0440.5801.0000.1260.1780.0360.2660.1550.2300.1400.2350.4790.1650.1980.0410.0980.1690.0880.1860.0670.1560.3500.0540.2130.141-0.0440.0210.1600.2550.1300.049-0.0080.0480.2230.220-0.0430.4240.1080.0840.1740.1270.0940.0830.5210.0920.1800.0600.0000.212-0.1610.127
Foundation0.1230.1960.0780.2480.1060.4150.4080.1460.1080.5310.4130.1070.0760.5610.2150.3580.1090.1170.1980.2340.1430.3680.3810.3810.1820.1540.1261.0000.2860.1880.4070.1820.3050.2480.2890.3040.1610.1370.2910.3340.3460.1640.3420.0960.0390.0590.0600.1080.110-0.0730.0690.2700.2490.2130.133-0.0870.0070.4570.356-0.3690.5400.2350.0040.0000.0580.0970.1900.5480.238-0.0720.0570.0000.236-0.6810.121
FullBath0.2570.4120.0000.0240.4470.1420.0970.1570.0350.2220.0400.2650.1640.3420.1880.1020.0480.0650.1460.1070.0750.3190.2320.2190.1640.1930.1780.2861.0000.0000.4460.1170.2650.1050.2600.6580.2300.1160.0000.1990.2820.1140.2780.1110.1240.2330.0440.1920.1050.0010.1990.1800.2910.1750.041-0.0480.0700.3020.373-0.2600.5780.0980.0430.1170.1170.0650.1570.6360.176-0.0360.0210.0000.229-0.5250.011
Functional0.0620.0000.0000.0050.0000.1970.1220.0200.1260.2000.1530.0000.0000.1750.1060.0730.0000.0000.0580.0000.1480.1310.1180.1380.0730.0710.0360.1880.0001.0000.0560.0280.1370.0390.104-0.0710.0580.0000.0560.0650.0760.0000.1300.0410.112-0.0360.0060.0240.000-0.0460.0280.0850.0960.1280.080-0.0960.0190.1770.0790.0540.1750.0770.0190.0000.0500.0600.0740.1370.075-0.0390.0000.0000.004-0.1600.000
GarageArea0.4900.0990.037-0.0820.1160.071-0.2550.242-0.006-0.0550.0350.201-0.022-0.4360.1090.2120.0260.0240.250-0.1780.128-0.4550.0770.0780.051-0.0550.2660.4070.4460.0561.0000.7130.6230.7190.5280.4690.1610.9970.1050.1400.1830.0930.3320.1090.0400.3680.0590.3450.157-0.048-0.0460.2100.3650.2440.089-0.0350.0310.2020.337-0.2010.5400.2700.0430.2130.1070.1030.1870.6490.2280.0300.2590.0000.250-0.6550.118
GarageCond0.1240.0570.0000.1540.1250.1820.0900.0790.0000.1250.0620.0940.0530.1970.0630.3370.0630.0380.1460.1340.1690.1930.1090.0990.0210.1550.1550.1820.1170.0280.7131.0000.7160.8000.7240.1810.1181.0000.1470.1300.2220.1560.2220.0420.0000.1490.0290.1470.068-0.122-0.0710.1200.1920.1350.000-0.0140.0120.2320.123-0.0380.2960.283-0.0100.0200.0440.0740.0830.3510.0460.0790.0000.0000.149-0.4380.047
GarageFinish0.2110.1820.0240.1710.1230.1230.1590.1700.0130.2220.0850.1130.0610.3400.1320.3250.0980.0000.1830.1200.1430.3370.2540.2470.1130.2360.2300.3050.2650.1370.6230.7161.0000.7180.684-0.3000.1970.9990.1420.2400.3060.1420.3190.1020.000-0.1380.039-0.1890.1430.029-0.0480.212-0.2630.2220.0000.0210.0180.334-0.2970.216-0.4380.269-0.0250.0000.0000.1130.164-0.4770.1890.0060.0000.000-0.2270.3950.053
GarageQual0.1200.0520.0000.1260.1430.1160.0890.0990.0000.1370.0690.1090.0560.2070.1110.3060.0640.1000.1290.1410.1560.1850.1470.1380.0000.1420.1400.2480.1050.0390.7190.8000.7181.0000.7240.1500.1101.0000.1300.1190.2130.1560.2150.0560.0000.1500.0240.1540.081-0.113-0.0690.1610.2200.1560.021-0.0300.0210.2770.106-0.0760.2620.270-0.0080.0000.0000.0470.0850.3270.0460.0520.0000.0000.136-0.4470.077
GarageType0.1950.2850.0000.2820.1720.1070.1340.1410.0000.1810.0720.1250.0350.2370.1060.3580.0870.0680.1790.1130.1150.2810.2100.2100.0840.2020.2350.2890.2600.1040.5280.7240.6840.7241.000-0.2550.2260.9990.1740.1580.2380.1480.2640.1070.110-0.2640.067-0.3000.1460.1610.1520.266-0.3020.2160.0400.0290.0030.277-0.2300.130-0.4300.290-0.0360.0000.0430.0730.145-0.5190.145-0.0340.2000.161-0.2590.4350.106
GrLivArea0.4960.6450.034-0.0090.5430.016-0.0830.054-0.0510.1260.0810.006-0.026-0.3580.2530.1030.0700.0220.142-0.0510.061-0.4150.1320.1100.067-0.0530.4790.3040.658-0.0710.4690.181-0.3000.150-0.2551.0000.3010.2460.0430.1430.1310.0000.2670.1000.0370.4490.0720.3290.2220.0640.2060.1360.3230.1570.000-0.0490.0850.1420.400-0.1520.6040.0940.0690.4700.2740.0950.1160.7310.1260.0870.0000.0000.227-0.3200.000
HalfBath0.1230.4490.0000.0000.2510.0940.1050.0150.0000.0980.0920.1520.1540.1730.1210.1300.0730.0700.0890.0760.0510.1500.1290.1230.0480.1730.1650.1610.2300.0580.1610.1180.1970.1100.2260.3011.0000.1520.0310.0950.2680.1930.1450.0000.0410.1460.0270.0940.086-0.0190.2810.0950.1700.1010.029-0.032-0.0040.2210.268-0.0720.2980.0840.0280.0000.0000.1000.1110.3420.0480.0600.0000.0000.107-0.2160.049
HasGarage0.1420.0790.0000.0340.1620.1350.0910.0810.0000.1160.0970.1260.0760.1540.0500.2440.0930.0000.1270.0940.2150.2480.1150.0960.0000.2000.1980.1370.1160.0000.9971.0000.9991.0000.9990.2460.1521.0000.0710.1360.2810.1960.2780.0710.0000.1590.0310.1280.071-0.126-0.0580.1040.1600.1580.000-0.0010.0190.2320.094-0.0120.2540.3340.0170.0000.0000.0590.1070.3010.0430.0720.0000.0000.116-0.3980.047
Heating0.0000.0490.0000.1040.1040.1850.1510.0000.0300.1630.1420.0540.0000.1660.0000.4430.0370.0000.1960.1820.0000.0670.1680.1760.0000.0350.0410.2910.0000.0560.1050.1470.1420.1300.1740.0430.0310.0711.0000.3670.1760.0690.2180.0000.000-0.0130.049-0.0470.0710.0150.0520.101-0.0910.0850.000-0.0030.0070.180-0.056-0.054-0.0940.173-0.0100.0000.0000.0190.027-0.1210.0380.0230.0000.000-0.1000.1750.005
HeatingQC0.0920.1100.0000.0930.0190.1020.0950.0590.0000.2090.1010.0580.0290.2400.0990.3780.0800.0330.1910.1270.0620.3230.2520.2530.1480.1160.0980.3340.1990.0650.1400.1300.2400.1190.1580.1430.0950.1360.3671.0000.2630.0940.3170.0540.049-0.0710.026-0.0660.0550.0200.0010.125-0.1700.1540.0270.033-0.0030.255-0.2900.105-0.4570.1740.0270.0000.0000.0140.168-0.4700.1900.0580.0170.027-0.1610.4780.037
House Category0.1740.1210.0000.1120.2240.1450.1810.0830.0390.2300.1320.1860.0930.3560.1390.3670.1440.0730.2410.0700.0880.3700.2810.2770.1760.2330.1690.3460.2820.0760.1830.2220.3060.2130.2380.1310.2680.2810.1760.2631.0000.5310.3880.0830.054-0.1790.106-0.1250.1270.0030.0020.294-0.0750.2430.098-0.002-0.0280.267-0.231-0.129-0.3380.171-0.0490.0000.0000.0420.312-0.4180.3360.0040.1160.000-0.2530.2740.197
KitchenAbvGr0.0290.0000.0000.0000.2330.1850.1750.0000.0000.1760.1640.1320.4980.1860.0630.2470.0920.1010.1210.0260.0000.0910.0600.0670.0410.0790.0880.1640.1140.0000.0930.1560.1420.1560.1480.0000.1930.1960.0690.0940.5311.0000.1010.0000.000-0.0280.052-0.0050.042-0.0040.2840.067-0.0500.0070.0000.0310.0320.123-0.110-0.095-0.1940.135-0.0150.0000.1890.1220.145-0.1680.049-0.0520.0000.000-0.0970.1500.039
KitchenQual0.2510.1830.0000.0570.1310.1370.1510.2080.0390.2810.0870.0940.0000.4190.1930.3420.0920.0710.2360.0910.1770.5440.2570.2600.1210.2590.1860.3420.2780.1300.3320.2220.3190.2150.2640.2670.1450.2780.2180.3170.3880.1011.0000.0970.045-0.1300.011-0.1440.0950.028-0.0300.154-0.2380.2240.0990.069-0.0480.364-0.3380.111-0.5720.191-0.0580.0550.0150.1440.209-0.5680.2490.0070.0610.000-0.2200.4880.080
LandContour0.0960.0580.0730.1100.1110.0610.1930.1390.0480.0870.0000.1050.0260.0930.0640.1280.0290.0690.0690.0270.0000.1340.1260.1230.0530.0750.0670.0960.1110.0410.1090.0420.1020.0560.1070.1000.0000.0710.0000.0540.0830.0000.0971.0000.457-0.0810.0660.0030.126-0.046-0.0030.0510.0890.0880.0000.006-0.0310.1710.057-0.0320.0160.116-0.0110.0000.2420.1280.072-0.0120.069-0.0080.1140.000-0.016-0.0910.150
LandSlope0.0000.0000.0640.0000.1010.1330.2220.0840.1410.0410.0750.2000.0450.0000.0530.0000.0000.0000.0000.0000.0000.0930.1430.1260.0140.0320.1560.0390.1240.1120.0400.0000.0000.0000.1100.0370.0410.0000.0000.0490.0540.0000.0450.4571.0000.1180.0840.0260.1190.014-0.0210.014-0.0410.0480.0470.036-0.0030.115-0.0260.019-0.0490.000-0.0170.0000.2710.1650.0300.0510.0380.0650.1760.0000.0720.0810.114
LotArea0.4420.1230.063-0.1300.3390.023-0.1770.1720.073-0.047-0.0470.0960.046-0.1310.0750.1060.0670.0210.078-0.0700.033-0.1300.0790.0780.059-0.0520.3500.0590.233-0.0360.3680.149-0.1380.150-0.2640.4490.1460.159-0.013-0.071-0.179-0.028-0.130-0.0810.1181.0000.0910.5530.266-0.020-0.2660.0000.1780.1030.1800.0600.0070.0000.178-0.0440.2340.0340.0850.1330.3600.1190.0000.4580.0000.0930.2910.0000.186-0.1020.112
LotConfig0.0650.0750.0680.0760.0250.0000.0590.0580.0310.0660.0000.0000.0400.0730.0120.0680.0990.0210.0420.0670.0000.0190.0920.1000.0230.0570.0540.0600.0440.0060.0590.0290.0390.0240.0670.0720.0270.0310.0490.0260.1060.0520.0110.0660.0840.0911.000-0.1540.221-0.0200.0530.0890.0130.0000.000-0.0180.0110.083-0.021-0.051-0.0150.039-0.0530.0280.1070.0440.009-0.0600.000-0.0160.0000.089-0.008-0.0200.260
LotFrontage0.3860.0450.053-0.2190.2930.111-0.1350.1520.053-0.062-0.0280.0790.001-0.1140.0960.120-0.0130.0050.096-0.1000.080-0.1550.0270.0530.026-0.0230.2130.1080.1920.0240.3450.147-0.1890.154-0.3000.3290.0940.128-0.047-0.066-0.125-0.005-0.1440.0030.0260.553-0.1541.0000.243-0.035-0.2790.2660.2450.1360.0520.0230.0230.1550.151-0.0720.2300.0580.0770.3420.2770.1340.0870.3760.0890.0400.1030.0000.100-0.1380.360
LotShape0.2070.1490.0160.1150.0320.0600.1060.2060.0560.0650.0650.0940.0450.1430.0410.1080.0640.0750.0770.0600.0000.1140.0950.1030.0430.1220.1410.1100.1050.0000.1570.0680.1430.0810.1460.2220.0860.0710.0710.0550.1270.0420.0950.1260.1190.2660.2210.2431.0000.0430.0720.192-0.1050.0680.000-0.023-0.0360.182-0.1270.019-0.2010.076-0.0240.1070.1040.0000.030-0.3090.000-0.0470.0340.000-0.1720.1880.333
LowQualFinSF-0.0390.0580.0220.0940.021-0.0440.069-0.0790.0010.090-0.008-0.061-0.0110.0830.020-0.048-0.0230.089-0.0340.048-0.0490.0150.0180.008-0.017-0.007-0.044-0.0730.001-0.046-0.048-0.1220.029-0.1130.1610.064-0.019-0.1260.0150.0200.003-0.0040.028-0.0460.014-0.020-0.020-0.0350.0431.0000.0760.154-0.1070.0000.0980.029-0.0040.0610.0110.039-0.0340.0920.0660.1170.0800.0000.000-0.0670.000-0.0190.0000.000-0.0420.0680.000
MSSubClass-0.2750.488-0.0360.1330.070-0.038-0.066-0.107-0.0840.0800.065-0.0410.002-0.121-0.115-0.1070.0260.0480.0460.0130.013-0.055-0.050-0.0890.0250.0730.0210.0690.1990.028-0.046-0.071-0.048-0.0690.1520.2060.281-0.0580.0520.0010.0020.284-0.030-0.003-0.021-0.2660.053-0.2790.0720.0761.0000.3360.0260.1620.060-0.0330.0200.2510.033-0.0740.1110.1760.0330.0900.0870.1690.1700.0090.116-0.0220.1030.0000.023-0.0520.108
MSZoning0.2320.2260.0000.3680.1680.0780.0980.1400.0470.1640.0750.0930.0340.1860.1050.2040.0480.0620.1060.1570.0880.1940.2230.2350.0490.1680.1600.2700.1800.0850.2100.1200.2120.1610.2660.1360.0950.1040.1010.1250.2940.0670.1540.0510.0140.0000.0890.2660.1920.1540.3361.000-0.0930.1150.0000.003-0.0330.660-0.1780.185-0.2090.175-0.0150.0000.0440.0770.126-0.3350.113-0.0110.0650.000-0.0790.2770.103
MasVnrArea0.3520.0640.041-0.1230.1130.099-0.1620.241-0.062-0.0860.0900.1210.033-0.2660.0750.175-0.021-0.0240.154-0.1800.108-0.317-0.042-0.0460.055-0.0420.2550.2490.2910.0960.3650.192-0.2630.220-0.3020.3230.1700.160-0.091-0.170-0.075-0.050-0.2380.089-0.0410.1780.0130.245-0.105-0.1070.026-0.0931.0000.4030.000-0.0500.0170.1410.208-0.1800.4130.0760.0050.0300.1410.1870.0980.4220.1370.0380.0000.1700.174-0.3380.000
MasVnrType0.2130.1120.0000.1290.0530.0600.1260.1660.0950.2010.0720.0850.0520.2540.1680.1790.0390.0000.1090.0980.0640.2490.2120.2010.0970.2000.1300.2130.1750.1280.2440.1350.2220.1560.2160.1570.1010.1580.0850.1540.2430.0070.2240.0880.0480.1030.0000.1360.0680.0000.1620.1150.4031.0000.0740.0210.0020.324-0.0210.022-0.0810.1490.0070.0000.0000.1600.197-0.0930.235-0.0300.0000.000-0.0530.0190.133
MiscFeature0.0230.0000.1140.0000.0000.0000.0240.0000.0370.0770.0280.0340.0000.0760.0000.0000.0000.0210.0030.0000.1230.1070.0950.1000.1200.0820.0490.1330.0410.0800.0890.0000.0000.0210.0400.0000.0290.0000.0000.0270.0980.0000.0990.0000.0470.1800.0000.0520.0000.0980.0600.0000.0000.0741.0000.9800.0160.080-0.0370.087-0.0960.0000.0390.0000.0000.0000.043-0.0690.0580.0110.1270.0000.0110.0660.039
MiscVal-0.033-0.0050.0050.0120.013-0.0080.0360.0060.030-0.058-0.051-0.0060.0320.054-0.045-0.009-0.0120.0550.0330.039-0.0180.047-0.015-0.025-0.0260.101-0.008-0.087-0.048-0.096-0.035-0.0140.021-0.0300.029-0.049-0.032-0.001-0.0030.033-0.0020.0310.0690.0060.0360.060-0.0180.023-0.0230.029-0.0330.003-0.0500.0210.9801.0000.0110.000-0.0340.086-0.0880.0890.0410.1570.0000.1220.000-0.0620.0000.0150.0000.0000.0170.0630.062
MoSold0.0510.0490.037-0.0190.057-0.023-0.019-0.017-0.0260.0400.029-0.0270.037-0.0080.0370.0150.0050.022-0.004-0.0260.020-0.040-0.0000.0060.028-0.0520.0480.0070.0700.0190.0310.0120.0180.0210.0030.085-0.0040.0190.007-0.003-0.0280.032-0.048-0.031-0.0030.0070.0110.023-0.036-0.0040.020-0.0330.0170.0020.0160.0111.0000.0370.062-0.0080.0590.000-0.0230.0470.0000.0000.0630.0700.0680.0240.0580.0470.039-0.0290.042
Neighborhood0.1460.1600.0000.4570.1320.1080.1800.1500.0720.2400.1260.1230.1040.4130.1380.2630.1840.0560.1960.1150.1060.4050.3020.3040.2010.2400.2230.4570.3020.1770.2020.2320.3340.2770.2770.1420.2210.2320.1800.2550.2670.1230.3640.1710.1150.0000.0830.1550.1820.0610.2510.6600.1410.3240.0800.0000.0371.0000.0440.0270.0540.233-0.0230.0660.0730.1530.2420.0480.2960.0260.0060.0000.0010.0260.247
OpenPorchSF0.2330.2280.0180.0330.1050.058-0.1010.077-0.0680.0240.1100.082-0.040-0.3430.1580.0910.0340.0060.148-0.1670.060-0.3810.1190.1010.034-0.0070.2200.3560.3730.0790.3370.123-0.2970.106-0.2300.4000.2680.094-0.056-0.290-0.231-0.110-0.3380.057-0.0260.178-0.0210.151-0.1270.0110.033-0.1780.208-0.021-0.037-0.0340.0620.0441.000-0.1320.4330.0340.0370.1080.0980.0580.0970.4770.1250.0070.0380.0450.125-0.3930.000
OverallCond-0.164-0.0000.0320.074-0.0030.0310.056-0.0070.101-0.095-0.062-0.0490.1150.305-0.1290.1000.0400.0560.0460.113-0.2270.261-0.089-0.074-0.045-0.000-0.043-0.369-0.2600.054-0.201-0.0380.216-0.0760.130-0.152-0.072-0.012-0.0540.105-0.129-0.0950.111-0.0320.019-0.044-0.051-0.0720.0190.039-0.0740.185-0.1800.0220.0870.086-0.0080.027-0.1321.000-0.1770.188-0.0060.0000.0000.0520.147-0.1270.1750.0740.0680.000-0.0440.3400.037
OverallQual0.4080.2920.034-0.0630.1260.113-0.2690.130-0.1160.0730.1950.095-0.050-0.5830.2740.2480.080-0.0260.248-0.1620.128-0.6690.1530.1410.073-0.1490.4240.5400.5780.1750.5400.296-0.4380.262-0.4300.6040.2980.254-0.094-0.457-0.338-0.194-0.5720.016-0.0490.234-0.0150.230-0.201-0.0340.111-0.2090.413-0.081-0.096-0.0880.0590.0540.433-0.1771.0000.1750.0570.1660.1180.2040.2000.8090.2460.0480.0720.0000.263-0.6370.099
PavedDrive0.1040.1120.0000.2210.0980.1690.1120.1080.0000.1920.0940.0850.0170.1940.0950.3350.0940.0270.1840.1940.1540.1920.1690.1550.0080.1210.1080.2350.0980.0770.2700.2830.2690.2700.2900.0940.0840.3340.1730.1740.1710.1350.1910.1160.0000.0340.0390.0580.0760.0920.1760.1750.0760.1490.0000.0890.0000.2330.0340.1880.1751.0000.0210.0000.0160.0790.0590.2800.0520.0440.0000.0000.126-0.3250.037
PoolArea0.0710.062-0.009-0.0180.0720.023-0.0460.0580.068-0.039-0.0590.0700.026-0.017-0.0370.0180.023-0.0070.0210.004-0.033-0.032-0.012-0.0330.048-0.0210.0840.0040.0430.0190.043-0.010-0.025-0.008-0.0360.0690.0280.017-0.0100.027-0.049-0.015-0.058-0.011-0.0170.085-0.0530.077-0.0240.0660.033-0.0150.0050.0070.0390.041-0.023-0.0230.037-0.0060.0570.0211.0000.9990.2310.1180.1000.0590.0000.0190.0000.0000.050-0.0030.037
PoolQC0.3830.2830.0000.0000.0700.0000.0260.3960.0750.0000.1450.0950.0000.0110.0000.0000.0470.0000.0000.3720.0000.0030.0900.0910.1270.1120.1740.0000.1170.0000.2130.0200.0000.0000.0000.4700.0000.0000.0000.0000.0000.0000.0550.0000.0000.1330.0280.3420.1070.1170.0900.0000.0300.0000.0000.1570.0470.0660.1080.0000.1660.0000.9991.0000.1000.0620.0720.0590.0000.0190.0000.0000.050-0.0030.000
RoofMatl0.3080.1390.0560.0000.1650.0560.2410.2000.1470.0150.0470.1870.1350.0000.0120.0000.0560.0000.0000.0000.0310.0000.2210.1950.0210.0890.1270.0580.1170.0500.1070.0440.0000.0000.0430.2740.0000.0000.0000.0000.0000.1890.0150.2420.2710.3600.1070.2770.1040.0800.0870.0440.1410.0000.0000.0000.0000.0730.0980.0000.1180.0160.2310.1001.0000.5010.0280.0840.0000.0780.0000.0000.0490.0440.029
RoofStyle0.2310.1600.0490.1080.0770.0000.0980.1560.1370.0440.0640.0930.0780.1970.1560.0440.0330.0000.0000.1250.0740.1880.1060.1090.0040.1180.0940.0970.0650.0600.1030.0740.1130.0470.0730.0950.1000.0590.0190.0140.0420.1220.1440.1280.1650.1190.0440.1340.0000.0000.1690.0770.1870.1600.0000.1220.0000.1530.0580.0520.2040.0790.1180.0620.5011.0000.0620.1510.0580.1010.0000.0000.0740.0080.008
SaleCondition0.1350.0500.0000.0110.0990.0770.1090.1070.0210.1400.0660.1360.0960.2490.1720.0870.0000.0000.0770.0890.0490.2340.1860.1890.1080.1610.0830.1900.1570.0740.1870.0830.1640.0850.1450.1160.1110.1070.0270.1680.3120.1450.2090.0720.0300.0000.0090.0870.0300.0000.1700.1260.0980.1970.0430.0000.0630.2420.0970.1470.2000.0590.1000.0720.0280.0621.0000.2730.733-0.0390.0450.0850.053-0.3670.135
SalePrice0.5770.2940.066-0.1280.2370.128-0.3230.300-0.037-0.0700.1220.223-0.011-0.5700.1860.3140.036-0.0500.295-0.2190.127-0.6310.1320.1300.075-0.1170.5210.5480.6360.1370.6490.351-0.4770.327-0.5190.7310.3420.301-0.121-0.470-0.418-0.168-0.568-0.0120.0510.458-0.0600.376-0.309-0.0670.009-0.3350.422-0.093-0.069-0.0620.0700.0480.477-0.1270.8090.2800.0590.0590.0840.1510.2731.0000.2540.1020.0000.0000.356-0.6320.111
SaleType0.1770.0550.0000.0000.0000.0840.1200.1250.1030.1640.0660.0000.0460.3020.1980.0800.0000.0000.0610.0630.0830.2890.2120.2150.1000.1910.0920.2380.1760.0750.2280.0460.1890.0460.1450.1260.0480.0430.0380.1900.3360.0490.2490.0690.0380.0000.0000.0890.0000.0000.1160.1130.1370.2350.0580.0000.0680.2960.1250.1750.2460.0520.0000.0000.0000.0580.7330.2541.0000.0240.0120.110-0.0100.3320.127
ScreenPorch0.1080.012-0.038-0.0430.0340.030-0.0170.0730.058-0.039-0.0270.0310.0380.029-0.0120.039-0.004-0.0300.000-0.081-0.0130.0300.0010.013-0.008-0.0400.180-0.072-0.036-0.0390.0300.0790.0060.052-0.0340.0870.0600.0720.0230.0580.004-0.0520.007-0.0080.0650.093-0.0160.040-0.047-0.019-0.022-0.0110.038-0.0300.0110.0150.0240.0260.0070.0740.0480.0440.0190.0190.0780.101-0.0390.1020.0241.0000.0750.214-0.0910.0630.000
Street0.0000.0000.0000.0000.0000.0000.0870.0210.0480.0000.1040.0910.0000.0000.0000.0400.0000.0000.0000.0000.0000.3210.0320.0300.0000.0000.0600.0570.0210.0000.2590.0000.0000.0000.2000.0000.0000.0000.0000.0170.1160.0000.0610.1140.1760.2910.0000.1030.0340.0000.1030.0650.0000.0000.1270.0000.0580.0060.0380.0680.0720.0000.0000.0000.0000.0000.0450.0000.0120.0751.0000.0000.014-0.0440.000
Utilities0.0000.0000.0000.0000.0000.0000.0000.0000.1840.0000.1170.0000.1020.0000.0000.0000.0000.0000.0930.0000.0000.0000.0190.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1610.0000.0000.0000.0000.0270.0000.0000.0000.0000.0000.0000.0890.0000.0000.0000.0000.0000.1700.0000.0000.0000.0470.0000.0450.0000.0000.0000.0000.0000.0000.0000.0850.0000.1100.2140.0001.000-0.0240.0240.000
WoodDeckSF0.2220.070-0.028-0.1290.0550.054-0.2860.1800.069-0.094-0.0190.1700.049-0.268-0.0340.167-0.0240.0080.181-0.1580.028-0.2340.0380.0290.019-0.0280.2120.2360.2290.0040.2500.149-0.2270.136-0.2590.2270.1070.116-0.100-0.161-0.253-0.097-0.220-0.0160.0720.186-0.0080.100-0.172-0.0420.023-0.0790.174-0.0530.0110.0170.0390.0010.125-0.0440.2630.1260.0500.0500.0490.0740.0530.356-0.010-0.0910.014-0.0241.000-0.2960.086
YearsSinceGarage-0.270-0.091-0.0300.1230.035-0.1050.278-0.1110.119-0.029-0.158-0.1030.0710.628-0.184-0.316-0.0210.023-0.3240.310-0.2120.628-0.138-0.149-0.0420.087-0.161-0.681-0.525-0.160-0.655-0.4380.395-0.4470.435-0.320-0.216-0.3980.1750.4780.2740.1500.488-0.0910.081-0.102-0.020-0.1380.1880.068-0.0520.277-0.3380.0190.0660.063-0.0290.026-0.3930.340-0.637-0.325-0.003-0.0030.0440.008-0.367-0.6320.3320.063-0.0440.024-0.2961.0000.047
fillna_LotFrontage0.0690.0700.0000.0740.0590.0460.0070.0970.0820.0980.0690.0540.0190.1110.0900.0630.0000.0000.0310.0350.0270.0260.1750.1950.0000.1740.1270.1210.0110.0000.1180.0470.0530.0770.1060.0000.0490.0470.0050.0370.1970.0390.0800.1500.1140.1120.2600.3600.3330.0000.1080.1030.0000.1330.0390.0620.0420.2470.0000.0370.0990.0370.0370.0000.0290.0080.1350.1110.1270.0000.0000.0000.0860.0471.000

Missing values

2024-05-03T04:25:34.055758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-03T04:25:34.878754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

MSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2OverallQualOverallCondRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualFunctionalFireplacesFireplaceQuGarageTypeGarageFinishGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldSaleTypeSaleConditionSalePriceHouse CategoryHasGarageYearsSinceGaragefillna_LotFrontage
060RL65.08450PaveNoAlleyRegLvlAllPubInsideGtlCollgCrNormNorm75GableCompShgVinylSdVinylSdBrkFace196.0GdTAPConcGdTANoGLQ706Unf0150GasAExYSBrkr85685401710102131GdTyp0NoFireplaceAttchdRFn548TATAY0610000NoPoolNoFenceNoMisc02WDNormal208500Modern Family Home15.00
120RL80.09600PaveNoAlleyRegLvlAllPubOtherGtlOtherFeedrNorm68GableCompShgMetalSdMetalSdNoMasVnr0.0TATACBlockGdTAGdALQ978Unf0284GasAExYSBrkr1262001262012031TATyp1TAAttchdRFn460TATAY29800000NoPoolNoFenceNoMisc05WDNormal181500Contemporary Family Home131.00
260RL68.011250PaveNoAlleyIR1LvlAllPubInsideGtlCollgCrNormNorm75GableCompShgVinylSdVinylSdBrkFace162.0GdTAPConcGdTAMnGLQ486Unf0434GasAExYSBrkr92086601786102131GdTyp1TAAttchdRFn608TATAY0420000NoPoolNoFenceNoMisc09WDNormal223500Contemporary Family Home17.00
370RL60.09550PaveNoAlleyIR1LvlAllPubCornerGtlOtherNormNorm75GableCompShgWd SdngOtherNoMasVnr0.0TATABrkTilTAGdNoALQ216Unf0540GasAGdYSBrkr96175601717101031GdTyp1GdDetchdUnf642TATAY035272000NoPoolNoFenceNoMisc02WDAbnorml140000Vintage Family Home18.00
460RL84.014260PaveNoAlleyIR1LvlAllPubOtherGtlOtherNormNorm85GableCompShgVinylSdVinylSdBrkFace350.0GdTAPConcGdTAAvGLQ655Unf0490GasAExYSBrkr1145105302198102141GdTyp1TAAttchdRFn836TATAY192840000NoPoolNoFenceNoMisc012WDNormal250000Contemporary Family Home18.00
550RL85.014115PaveNoAlleyIR1LvlAllPubInsideGtlOtherNormNorm55GableCompShgVinylSdVinylSdNoMasVnr0.0TATAOtherGdTANoGLQ732Unf064GasAExYSBrkr79656601362101111TATyp0NoFireplaceAttchdUnf480TATAY4030032000NoPoolMnPrvOther70010WDNormal143000Contemporary Family Home116.00
620RL75.010084PaveNoAlleyRegLvlAllPubInsideGtlSomerstNormNorm85GableCompShgVinylSdVinylSdStone186.0GdTAPConcExTAAvGLQ1369Unf0317GasAExYSBrkr1694001694102031GdTyp1GdAttchdRFn636TATAY255570000NoPoolNoFenceNoMisc08WDNormal307000Modern Family Home13.00
760RL69.010382PaveNoAlleyIR1LvlAllPubCornerGtlNWAmesOtherNorm76GableCompShgHdBoardHdBoardStone240.0TATACBlockGdTAMnALQ859BLQ32216GasAExYSBrkr110798302090102131TATyp2TAAttchdRFn484TATAY235204228000NoPoolNoFenceOther35011WDNormal200000Contemporary Family Home136.01
850RM51.06120PaveNoAlleyRegLvlAllPubInsideGtlOldTownOtherNorm75GableCompShgOtherOtherNoMasVnr0.0TATABrkTilTATANoUnf0Unf0952GasAGdYOther102275201774002022TAOther2TADetchdUnf468OtherTAY900205000NoPoolNoFenceNoMisc04WDAbnorml129900Vintage Family Home177.00
9190RL50.07420PaveNoAlleyRegLvlAllPubCornerGtlOtherOtherOther56GableCompShgMetalSdMetalSdNoMasVnr0.0TATABrkTilTATANoGLQ851Unf0140GasAExYSBrkr1077001077101022TATyp2TAAttchdRFn205OtherTAY040000NoPoolNoFenceNoMisc01WDNormal118000Vintage Multi-Family or Duplex169.00
MSSubClassMSZoningLotFrontageLotAreaStreetAlleyLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2OverallQualOverallCondRoofStyleRoofMatlExterior1stExterior2ndMasVnrTypeMasVnrAreaExterQualExterCondFoundationBsmtQualBsmtCondBsmtExposureBsmtFinType1BsmtFinSF1BsmtFinType2BsmtFinSF2BsmtUnfSFHeatingHeatingQCCentralAirElectrical1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualFunctionalFireplacesFireplaceQuGarageTypeGarageFinishGarageAreaGarageQualGarageCondPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaPoolQCFenceMiscFeatureMiscValMoSoldSaleTypeSaleConditionSalePriceHouse CategoryHasGarageYearsSinceGaragefillna_LotFrontage
145090RL60.09000PaveNoAlleyRegLvlAllPubOtherGtlNAmesNormNorm55GableCompShgVinylSdVinylSdNoMasVnr0.0TATACBlockGdTANoUnf0Unf0896GasATAYSBrkr89689601792002242TATyp0NoFireplaceNoGarageNoGarage0NoGarageNoGarageY32450000NoPoolNoFenceNoMisc09WDNormal136000Contemporary Multi-Family or Duplex02010.00
145120RL78.09262PaveNoAlleyRegLvlAllPubInsideGtlSomerstNormNorm85GableCompShgOtherOtherStone194.0GdTAPConcGdTANoUnf0Unf01573GasAExYSBrkr1578001578002031ExTyp1GdAttchdFin840TATAY0360000NoPoolNoFenceNoMisc05NewPartial287090Modern Family Home11.00
1452180RM35.03675PaveNoAlleyRegLvlAllPubInsideGtlEdwardsNormNorm55GableCompShgVinylSdVinylSdBrkFace80.0TATAPConcGdTAGdGLQ547Unf00GasAGdYSBrkr1072001072101021TATyp0NoFireplaceOtherFin525TATAY0280000NoPoolNoFenceNoMisc05WDNormal145000Modern Townhouse11.00
145320RL90.017217PaveNoAlleyRegLvlAllPubInsideGtlOtherNormNorm55GableCompShgVinylSdVinylSdNoMasVnr0.0TATAPConcGdTANoUnf0Unf01140GasAExYSBrkr1140001140001031TATyp0NoFireplaceNoGarageNoGarage0NoGarageNoGarageY36560000NoPoolNoFenceNoMisc07WDAbnorml84500Modern Family Home02007.00
145420Other62.07500PaveOtherRegLvlAllPubInsideGtlSomerstNormNorm75GableCompShgVinylSdVinylSdNoMasVnr0.0GdTAPConcGdTANoGLQ410Unf0811GasAExYSBrkr1221001221102021GdTyp0NoFireplaceAttchdRFn400TATAY01130000NoPoolNoFenceNoMisc010WDNormal185000Modern Family Home15.00
145560RL62.07917PaveNoAlleyRegLvlAllPubInsideGtlGilbertNormNorm65GableCompShgVinylSdVinylSdNoMasVnr0.0TATAPConcGdTANoUnf0Unf0953GasAExYSBrkr95369401647002131TATyp1TAAttchdRFn460TATAY0400000NoPoolNoFenceNoMisc08WDNormal175000Contemporary Family Home18.00
145620RL85.013175PaveNoAlleyRegLvlAllPubInsideGtlNWAmesNormNorm66GableCompShgPlywoodPlywoodStone119.0TATACBlockGdTANoALQ790Rec163589GasATAYSBrkr2073002073102031TAOther2TAAttchdUnf500TATAY34900000NoPoolMnPrvNoMisc02WDNormal210000Contemporary Family Home132.00
145770RL66.09042PaveNoAlleyRegLvlAllPubInsideGtlOtherNormNorm79GableCompShgOtherOtherNoMasVnr0.0ExGdOtherTAGdNoGLQ275Unf0877GasAExYSBrkr1188115202340002041GdTyp2GdAttchdRFn252TATAY0600000NoPoolOtherOther25005WDNormal266500Modern Family Home169.00
145820RL68.09717PaveNoAlleyRegLvlAllPubInsideGtlNAmesNormNorm56HipCompShgMetalSdMetalSdNoMasVnr0.0TATACBlockTATAMnGLQ49Rec10290GasAGdYFuseA1078001078101021GdTyp0NoFireplaceAttchdUnf240TATAY3660112000NoPoolNoFenceNoMisc04WDNormal142125Contemporary Family Home160.00
145920RL75.09937PaveNoAlleyRegLvlAllPubInsideGtlEdwardsNormNorm56GableCompShgHdBoardHdBoardNoMasVnr0.0GdTACBlockTATANoBLQ830LwQ290136GasAGdYSBrkr1256001256101131TATyp0NoFireplaceAttchdFin276TATAY736680000NoPoolNoFenceNoMisc06WDNormal147500Vintage Family Home143.00